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gpt-neo-125m-neurallinguisticpioneers

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metadata
license: mit
tags:
  - generated_from_trainer
model-index:
  - name: gpt-neo-125m-neurallinguisticpioneers
    results: []

gpt-neo-125m-neurallinguisticpioneers

This model is a fine-tuned version of EleutherAI/gpt-neo-125m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6584

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
5.8245 0.01 1 5.0320
5.1946 0.01 2 4.2171
4.1809 0.02 3 3.4349
3.3553 0.02 4 2.7171
2.185 0.03 5 2.0634
1.9955 0.03 6 1.5786
1.9371 0.04 7 1.2490
1.4402 0.04 8 1.0349
0.8763 0.05 9 0.9157
0.8813 0.05 10 0.8550
0.7723 0.06 11 0.8259
0.7909 0.06 12 0.8052
0.4889 0.07 13 0.7959
0.7361 0.07 14 0.7891
0.4922 0.08 15 0.7793
0.5533 0.09 16 0.7675
1.1071 0.09 17 0.7563
0.7885 0.1 18 0.7480
0.7701 0.1 19 0.7445
0.6235 0.11 20 0.7447
0.8623 0.11 21 0.7484
0.665 0.12 22 0.7558
0.6907 0.12 23 0.7573
0.7143 0.13 24 0.7583
0.7554 0.13 25 0.7599
0.6228 0.14 26 0.7621
0.8079 0.14 27 0.7612
0.6974 0.15 28 0.7586
0.8349 0.16 29 0.7541
0.8251 0.16 30 0.7484
0.687 0.17 31 0.7400
0.8156 0.17 32 0.7280
0.7693 0.18 33 0.7183
0.5224 0.18 34 0.7096
0.6345 0.19 35 0.7033
0.6443 0.19 36 0.6979
1.1552 0.2 37 0.6930
0.7819 0.2 38 0.6897
0.6277 0.21 39 0.6875
0.5751 0.21 40 0.6862
0.7169 0.22 41 0.6854
0.7077 0.22 42 0.6842
0.5667 0.23 43 0.6831
0.9234 0.24 44 0.6822
0.6332 0.24 45 0.6815
0.865 0.25 46 0.6806
0.5918 0.25 47 0.6797
0.6196 0.26 48 0.6788
0.7697 0.26 49 0.6778
0.4448 0.27 50 0.6769
0.7951 0.27 51 0.6760
0.9171 0.28 52 0.6751
0.7169 0.28 53 0.6745
0.7001 0.29 54 0.6742
0.7755 0.29 55 0.6742
0.7426 0.3 56 0.6743
0.6208 0.3 57 0.6742
0.6962 0.31 58 0.6740
0.3848 0.32 59 0.6739
0.6986 0.32 60 0.6736
0.6316 0.33 61 0.6734
0.5988 0.33 62 0.6732
0.6551 0.34 63 0.6729
0.6102 0.34 64 0.6724
0.7752 0.35 65 0.6718
0.6145 0.35 66 0.6713
0.6829 0.36 67 0.6709
0.7952 0.36 68 0.6705
0.5888 0.37 69 0.6702
0.7763 0.37 70 0.6698
0.6723 0.38 71 0.6694
0.6429 0.39 72 0.6691
1.0005 0.39 73 0.6688
0.6184 0.4 74 0.6684
0.7118 0.4 75 0.6682
0.5414 0.41 76 0.6679
0.6491 0.41 77 0.6676
0.9418 0.42 78 0.6673
0.7183 0.42 79 0.6670
0.682 0.43 80 0.6668
0.5946 0.43 81 0.6665
0.6681 0.44 82 0.6662
0.9125 0.44 83 0.6659
0.6752 0.45 84 0.6657
0.6908 0.45 85 0.6655
0.5878 0.46 86 0.6653
0.805 0.47 87 0.6651
0.7584 0.47 88 0.6650
0.6652 0.48 89 0.6649
0.9363 0.48 90 0.6647
0.6201 0.49 91 0.6646
0.6827 0.49 92 0.6644
0.8921 0.5 93 0.6643
0.5194 0.5 94 0.6641
0.9393 0.51 95 0.6639
0.8484 0.51 96 0.6637
0.5412 0.52 97 0.6635
1.0085 0.52 98 0.6633
0.5217 0.53 99 0.6632
0.6137 0.53 100 0.6630
0.5484 0.54 101 0.6629
0.5827 0.55 102 0.6627
0.3374 0.55 103 0.6629
0.8269 0.56 104 0.6630
0.8126 0.56 105 0.6630
0.8088 0.57 106 0.6631
0.5498 0.57 107 0.6632
0.6787 0.58 108 0.6633
0.8786 0.58 109 0.6633
0.6237 0.59 110 0.6634
0.6369 0.59 111 0.6634
0.5629 0.6 112 0.6634
0.4571 0.6 113 0.6635
0.902 0.61 114 0.6634
0.5153 0.61 115 0.6632
0.9284 0.62 116 0.6629
0.7149 0.63 117 0.6626
0.5224 0.63 118 0.6623
0.5969 0.64 119 0.6621
0.655 0.64 120 0.6619
0.6182 0.65 121 0.6619
0.6564 0.65 122 0.6618
0.6919 0.66 123 0.6618
0.5894 0.66 124 0.6617
0.4312 0.67 125 0.6617
0.7523 0.67 126 0.6617
0.7962 0.68 127 0.6617
0.3758 0.68 128 0.6617
0.7343 0.69 129 0.6617
0.7569 0.7 130 0.6616
0.4816 0.7 131 0.6616
0.7127 0.71 132 0.6616
0.4597 0.71 133 0.6616
0.6429 0.72 134 0.6616
0.6452 0.72 135 0.6616
0.5815 0.73 136 0.6615
0.743 0.73 137 0.6614
0.5613 0.74 138 0.6612
0.5038 0.74 139 0.6610
0.797 0.75 140 0.6609
0.6244 0.75 141 0.6608
0.4257 0.76 142 0.6607
0.6096 0.76 143 0.6606
0.6566 0.77 144 0.6605
0.4325 0.78 145 0.6604
0.7307 0.78 146 0.6604
0.7955 0.79 147 0.6603
0.6972 0.79 148 0.6602
0.7527 0.8 149 0.6602
0.5718 0.8 150 0.6602
0.8002 0.81 151 0.6602
0.6643 0.81 152 0.6602
0.7817 0.82 153 0.6602
0.6829 0.82 154 0.6602
0.8392 0.83 155 0.6601
0.5246 0.83 156 0.6601
0.6613 0.84 157 0.6601
0.4456 0.84 158 0.6600
0.4505 0.85 159 0.6600
0.6184 0.86 160 0.6600
0.6419 0.86 161 0.6599
0.3138 0.87 162 0.6599
0.5554 0.87 163 0.6598
0.702 0.88 164 0.6597
0.801 0.88 165 0.6595
0.6689 0.89 166 0.6594
0.5907 0.89 167 0.6593
0.9349 0.9 168 0.6592
0.7987 0.9 169 0.6591
0.6379 0.91 170 0.6590
0.5561 0.91 171 0.6589
0.6637 0.92 172 0.6589
0.5391 0.93 173 0.6588
0.6578 0.93 174 0.6588
0.7013 0.94 175 0.6587
0.6868 0.94 176 0.6587
0.6297 0.95 177 0.6586
0.7349 0.95 178 0.6586
0.8577 0.96 179 0.6585
0.8536 0.96 180 0.6585
0.4971 0.97 181 0.6585
0.5129 0.97 182 0.6585
0.7636 0.98 183 0.6585
0.5111 0.98 184 0.6585
0.7281 0.99 185 0.6585
0.5653 0.99 186 0.6585
0.7766 1.0 187 0.6584

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3