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lge_tests_prelim

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Safetensors
nanogpt
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  1. README.md +82 -82
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7046
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- - Accuracy: 0.89
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  ## Model description
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@@ -36,7 +36,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
@@ -49,85 +49,85 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | No log | 0 | 0 | 2.6041 | 0.0 |
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- | 2.5759 | 0.0128 | 100 | 2.5716 | 0.0 |
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- | 2.5431 | 0.0256 | 200 | 2.5401 | 0.0 |
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- | 2.4939 | 0.0384 | 300 | 2.4946 | 0.0 |
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- | 2.4394 | 0.0512 | 400 | 2.4409 | 0.0 |
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- | 2.4054 | 0.0640 | 500 | 2.3917 | 0.0 |
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- | 2.358 | 0.0768 | 600 | 2.3522 | 0.0 |
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- | 2.3347 | 0.0896 | 700 | 2.3202 | 0.0 |
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- | 2.2668 | 0.1024 | 800 | 2.2886 | 0.0 |
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- | 2.2672 | 0.1152 | 900 | 2.2555 | 0.0 |
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- | 2.2008 | 0.1280 | 1000 | 2.2229 | 0.0 |
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- | 2.2122 | 0.1408 | 1100 | 2.2021 | 0.0 |
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- | 2.1658 | 0.1536 | 1200 | 2.1683 | 0.0 |
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- | 2.1137 | 0.1665 | 1300 | 2.1486 | 0.0 |
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- | 2.1001 | 0.1793 | 1400 | 2.1288 | 0.0 |
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- | 2.1002 | 0.1921 | 1500 | 2.0900 | 0.0 |
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- | 2.0586 | 0.2049 | 1600 | 2.0488 | 0.0 |
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- | 2.0614 | 0.2177 | 1700 | 2.0373 | 0.0 |
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- | 1.9266 | 0.2305 | 1800 | 1.9368 | 0.0 |
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- | 1.9221 | 0.2433 | 1900 | 1.9320 | 0.0 |
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- | 1.8304 | 0.2561 | 2000 | 1.8094 | 0.005 |
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- | 1.7483 | 0.2689 | 2100 | 1.7452 | 0.01 |
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- | 1.6969 | 0.2817 | 2200 | 1.6949 | 0.005 |
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- | 1.6504 | 0.2945 | 2300 | 1.6370 | 0.03 |
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- | 1.5781 | 0.3073 | 2400 | 1.5857 | 0.0 |
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- | 1.5424 | 0.3201 | 2500 | 1.5904 | 0.015 |
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- | 1.5002 | 0.3329 | 2600 | 1.5054 | 0.03 |
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- | 1.4529 | 0.3457 | 2700 | 1.4718 | 0.085 |
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- | 1.4592 | 0.3585 | 2800 | 1.4271 | 0.115 |
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- | 1.4238 | 0.3713 | 2900 | 1.4112 | 0.12 |
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- | 1.3797 | 0.3841 | 3000 | 1.3719 | 0.135 |
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- | 1.3668 | 0.3969 | 3100 | 1.3646 | 0.12 |
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- | 1.3191 | 0.4097 | 3200 | 1.3303 | 0.15 |
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- | 1.3003 | 0.4225 | 3300 | 1.3101 | 0.23 |
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- | 1.2744 | 0.4353 | 3400 | 1.2733 | 0.235 |
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- | 1.2389 | 0.4481 | 3500 | 1.2386 | 0.345 |
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- | 1.2181 | 0.4609 | 3600 | 1.2098 | 0.27 |
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- | 1.2185 | 0.4738 | 3700 | 1.1906 | 0.31 |
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- | 1.1838 | 0.4866 | 3800 | 1.1732 | 0.355 |
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- | 1.1711 | 0.4994 | 3900 | 1.1513 | 0.395 |
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- | 1.1311 | 0.5122 | 4000 | 1.1274 | 0.445 |
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- | 1.1143 | 0.5250 | 4100 | 1.1042 | 0.49 |
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- | 1.0783 | 0.5378 | 4200 | 1.0734 | 0.58 |
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- | 1.0703 | 0.5506 | 4300 | 1.0540 | 0.56 |
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- | 1.0464 | 0.5634 | 4400 | 1.0415 | 0.39 |
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- | 1.0445 | 0.5762 | 4500 | 1.0150 | 0.57 |
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- | 0.9972 | 0.5890 | 4600 | 1.0007 | 0.66 |
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- | 0.9835 | 0.6018 | 4700 | 0.9798 | 0.71 |
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- | 0.9762 | 0.6146 | 4800 | 0.9745 | 0.62 |
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- | 0.9408 | 0.6274 | 4900 | 0.9448 | 0.785 |
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- | 0.9184 | 0.6402 | 5000 | 0.9427 | 0.625 |
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- | 0.9119 | 0.6530 | 5100 | 0.9229 | 0.66 |
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- | 0.9068 | 0.6658 | 5200 | 0.9005 | 0.755 |
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- | 0.9112 | 0.6786 | 5300 | 0.9036 | 0.65 |
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- | 0.8669 | 0.6914 | 5400 | 0.8721 | 0.77 |
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- | 0.8703 | 0.7042 | 5500 | 0.8597 | 0.77 |
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- | 0.8376 | 0.7170 | 5600 | 0.8428 | 0.815 |
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- | 0.8379 | 0.7298 | 5700 | 0.8324 | 0.835 |
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- | 0.8203 | 0.7426 | 5800 | 0.8170 | 0.84 |
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- | 0.8157 | 0.7554 | 5900 | 0.8021 | 0.83 |
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- | 0.7674 | 0.7682 | 6000 | 0.7916 | 0.845 |
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- | 0.7709 | 0.7810 | 6100 | 0.7801 | 0.88 |
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- | 0.7595 | 0.7939 | 6200 | 0.7714 | 0.84 |
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- | 0.7476 | 0.8067 | 6300 | 0.7584 | 0.87 |
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- | 0.7338 | 0.8195 | 6400 | 0.7497 | 0.865 |
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- | 0.7375 | 0.8323 | 6500 | 0.7422 | 0.86 |
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- | 0.7377 | 0.8451 | 6600 | 0.7351 | 0.875 |
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- | 0.757 | 0.8579 | 6700 | 0.7306 | 0.865 |
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- | 0.7341 | 0.8707 | 6800 | 0.7235 | 0.89 |
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- | 0.7128 | 0.8835 | 6900 | 0.7208 | 0.865 |
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- | 0.6991 | 0.8963 | 7000 | 0.7151 | 0.895 |
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- | 0.7139 | 0.9091 | 7100 | 0.7120 | 0.89 |
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- | 0.7126 | 0.9219 | 7200 | 0.7102 | 0.895 |
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- | 0.694 | 0.9347 | 7300 | 0.7076 | 0.895 |
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- | 0.7196 | 0.9475 | 7400 | 0.7065 | 0.885 |
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- | 0.7013 | 0.9603 | 7500 | 0.7055 | 0.89 |
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- | 0.7137 | 0.9731 | 7600 | 0.7049 | 0.89 |
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- | 0.6975 | 0.9859 | 7700 | 0.7046 | 0.89 |
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- | 0.7038 | 0.9987 | 7800 | 0.7046 | 0.89 |
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  ### 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:
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+ - Loss: 0.5579
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+ - Accuracy: 0.135
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22
  ## Model description
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36
  ### Training hyperparameters
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38
  The following hyperparameters were used during training:
39
+ - learning_rate: 0.0005
40
  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | No log | 0 | 0 | 2.6523 | 0.0 |
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+ | 2.5669 | 0.0128 | 100 | 2.5637 | 0.0 |
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+ | 2.4943 | 0.0256 | 200 | 2.4864 | 0.0 |
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+ | 2.4021 | 0.0384 | 300 | 2.4031 | 0.0 |
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+ | 2.3401 | 0.0512 | 400 | 2.3406 | 0.0 |
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+ | 2.3156 | 0.0640 | 500 | 2.2938 | 0.0 |
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+ | 2.2569 | 0.0768 | 600 | 2.2496 | 0.0 |
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+ | 2.2248 | 0.0896 | 700 | 2.1901 | 0.0 |
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+ | 2.086 | 0.1024 | 800 | 2.0841 | 0.0 |
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+ | 1.972 | 0.1152 | 900 | 1.9745 | 0.0 |
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+ | 1.8129 | 0.1280 | 1000 | 1.8243 | 0.0 |
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+ | 1.7484 | 0.1408 | 1100 | 1.7172 | 0.005 |
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+ | 1.6341 | 0.1536 | 1200 | 1.6579 | 0.01 |
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+ | 1.5526 | 0.1665 | 1300 | 1.5674 | 0.005 |
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+ | 1.4884 | 0.1793 | 1400 | 1.4907 | 0.0 |
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+ | 1.4881 | 0.1921 | 1500 | 1.4831 | 0.005 |
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+ | 1.3971 | 0.2049 | 1600 | 1.3808 | 0.01 |
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+ | 1.4136 | 0.2177 | 1700 | 1.3420 | 0.015 |
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+ | 1.3024 | 0.2305 | 1800 | 1.2975 | 0.005 |
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+ | 1.2759 | 0.2433 | 1900 | 1.2343 | 0.03 |
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+ | 1.2258 | 0.2561 | 2000 | 1.2064 | 0.015 |
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+ | 1.1598 | 0.2689 | 2100 | 1.1575 | 0.045 |
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+ | 1.1416 | 0.2817 | 2200 | 1.1200 | 0.05 |
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+ | 1.1077 | 0.2945 | 2300 | 1.1366 | 0.055 |
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+ | 1.0426 | 0.3073 | 2400 | 1.0908 | 0.06 |
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+ | 1.0327 | 0.3201 | 2500 | 1.0422 | 0.065 |
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+ | 1.007 | 0.3329 | 2600 | 1.0304 | 0.06 |
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+ | 0.9646 | 0.3457 | 2700 | 0.9692 | 0.055 |
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+ | 0.9753 | 0.3585 | 2800 | 0.9414 | 0.025 |
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+ | 0.9347 | 0.3713 | 2900 | 0.9204 | 0.065 |
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+ | 0.9098 | 0.3841 | 3000 | 0.9292 | 0.04 |
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+ | 0.8989 | 0.3969 | 3100 | 0.8623 | 0.08 |
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+ | 0.9563 | 0.4097 | 3200 | 0.9201 | 0.06 |
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+ | 0.8445 | 0.4225 | 3300 | 0.8373 | 0.06 |
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+ | 0.8749 | 0.4353 | 3400 | 0.8468 | 0.09 |
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+ | 0.8352 | 0.4481 | 3500 | 0.8081 | 0.065 |
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+ | 0.8211 | 0.4609 | 3600 | 0.7949 | 0.09 |
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+ | 0.7724 | 0.4738 | 3700 | 0.7693 | 0.095 |
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+ | 0.7512 | 0.4866 | 3800 | 0.7588 | 0.09 |
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+ | 0.7176 | 0.4994 | 3900 | 0.7478 | 0.125 |
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+ | 0.7964 | 0.5122 | 4000 | 0.7499 | 0.03 |
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+ | 0.7561 | 0.5250 | 4100 | 0.7439 | 0.13 |
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+ | 0.7499 | 0.5378 | 4200 | 0.7456 | 0.105 |
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+ | 0.6744 | 0.5506 | 4300 | 0.6926 | 0.07 |
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+ | 0.6792 | 0.5634 | 4400 | 0.6805 | 0.125 |
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+ | 0.698 | 0.5762 | 4500 | 0.6753 | 0.125 |
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+ | 0.6829 | 0.5890 | 4600 | 0.6684 | 0.105 |
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+ | 0.6635 | 0.6018 | 4700 | 0.6620 | 0.105 |
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+ | 0.746 | 0.6146 | 4800 | 0.6894 | 0.065 |
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+ | 0.6637 | 0.6274 | 4900 | 0.6491 | 0.115 |
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+ | 0.6611 | 0.6402 | 5000 | 0.6797 | 0.135 |
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+ | 0.6658 | 0.6530 | 5100 | 0.6332 | 0.09 |
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+ | 0.6122 | 0.6658 | 5200 | 0.6320 | 0.135 |
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+ | 0.6293 | 0.6786 | 5300 | 0.6283 | 0.14 |
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+ | 0.6018 | 0.6914 | 5400 | 0.6220 | 0.095 |
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+ | 0.6105 | 0.7042 | 5500 | 0.6366 | 0.11 |
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+ | 0.6009 | 0.7170 | 5600 | 0.6011 | 0.12 |
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+ | 0.5913 | 0.7298 | 5700 | 0.5990 | 0.135 |
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+ | 0.6506 | 0.7426 | 5800 | 0.6214 | 0.16 |
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+ | 0.6126 | 0.7554 | 5900 | 0.5888 | 0.12 |
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+ | 0.6004 | 0.7682 | 6000 | 0.5905 | 0.12 |
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+ | 0.5713 | 0.7810 | 6100 | 0.5928 | 0.14 |
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+ | 0.5824 | 0.7939 | 6200 | 0.5954 | 0.17 |
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+ | 0.5874 | 0.8067 | 6300 | 0.5803 | 0.125 |
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+ | 0.5803 | 0.8195 | 6400 | 0.5785 | 0.115 |
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+ | 0.5691 | 0.8323 | 6500 | 0.5756 | 0.12 |
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+ | 0.5866 | 0.8451 | 6600 | 0.5700 | 0.135 |
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+ | 0.59 | 0.8579 | 6700 | 0.5692 | 0.11 |
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+ | 0.5511 | 0.8707 | 6800 | 0.5656 | 0.135 |
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+ | 0.5794 | 0.8835 | 6900 | 0.5621 | 0.13 |
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+ | 0.5575 | 0.8963 | 7000 | 0.5633 | 0.13 |
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+ | 0.5506 | 0.9091 | 7100 | 0.5658 | 0.115 |
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+ | 0.5605 | 0.9219 | 7200 | 0.5628 | 0.125 |
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+ | 0.5643 | 0.9347 | 7300 | 0.5607 | 0.115 |
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+ | 0.5675 | 0.9475 | 7400 | 0.5604 | 0.12 |
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+ | 0.576 | 0.9603 | 7500 | 0.5587 | 0.14 |
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+ | 0.5842 | 0.9731 | 7600 | 0.5582 | 0.135 |
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+ | 0.5791 | 0.9859 | 7700 | 0.5580 | 0.145 |
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+ | 0.5667 | 0.9987 | 7800 | 0.5579 | 0.135 |
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  ### Framework versions
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