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lge_tests_prelim

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Safetensors
nanogpt
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  1. README.md +161 -83
  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: 0.5307
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- - Accuracy: 0.34
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  ## Model description
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@@ -47,87 +47,165 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
51
- |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | No log | 0 | 0 | 2.6404 | 0.0 |
53
- | 2.5674 | 0.0128 | 100 | 2.5647 | 0.0 |
54
- | 2.5021 | 0.0256 | 200 | 2.4965 | 0.0 |
55
- | 2.4106 | 0.0384 | 300 | 2.4111 | 0.0 |
56
- | 2.3432 | 0.0512 | 400 | 2.3422 | 0.0 |
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- | 2.32 | 0.0640 | 500 | 2.3004 | 0.0 |
58
- | 2.2412 | 0.0768 | 600 | 2.2452 | 0.0 |
59
- | 2.1721 | 0.0896 | 700 | 2.1679 | 0.0 |
60
- | 1.9939 | 0.1024 | 800 | 1.9887 | 0.0 |
61
- | 1.9089 | 0.1152 | 900 | 1.9041 | 0.0 |
62
- | 2.0517 | 0.1280 | 1000 | 1.8690 | 0.0 |
63
- | 1.854 | 0.1408 | 1100 | 1.7567 | 0.0 |
64
- | 1.7972 | 0.1536 | 1200 | 1.7314 | 0.0 |
65
- | 1.6798 | 0.1665 | 1300 | 1.7170 | 0.0 |
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- | 1.6579 | 0.1793 | 1400 | 1.6576 | 0.0 |
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- | 1.6968 | 0.1921 | 1500 | 1.6208 | 0.005 |
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- | 1.5677 | 0.2049 | 1600 | 1.6667 | 0.0 |
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- | 1.5288 | 0.2177 | 1700 | 1.5156 | 0.005 |
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- | 1.5954 | 0.2305 | 1800 | 1.5904 | 0.0 |
71
- | 1.473 | 0.2433 | 1900 | 1.5063 | 0.01 |
72
- | 1.4783 | 0.2561 | 2000 | 1.4800 | 0.01 |
73
- | 1.5276 | 0.2689 | 2100 | 1.4590 | 0.01 |
74
- | 1.3354 | 0.2817 | 2200 | 1.4401 | 0.02 |
75
- | 1.4443 | 0.2945 | 2300 | 1.3868 | 0.0 |
76
- | 1.3269 | 0.3073 | 2400 | 1.3720 | 0.025 |
77
- | 1.3306 | 0.3201 | 2500 | 1.3052 | 0.015 |
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- | 1.274 | 0.3329 | 2600 | 1.3153 | 0.015 |
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- | 1.2331 | 0.3457 | 2700 | 1.2486 | 0.02 |
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- | 1.2947 | 0.3585 | 2800 | 1.2650 | 0.01 |
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- | 1.1635 | 0.3713 | 2900 | 1.1717 | 0.03 |
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- | 1.112 | 0.3841 | 3000 | 1.1700 | 0.045 |
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- | 1.1343 | 0.3969 | 3100 | 1.1362 | 0.04 |
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- | 1.072 | 0.4097 | 3200 | 1.1037 | 0.055 |
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- | 1.0831 | 0.4225 | 3300 | 1.0751 | 0.02 |
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- | 1.0762 | 0.4353 | 3400 | 1.0773 | 0.035 |
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- | 0.9965 | 0.4481 | 3500 | 1.0021 | 0.015 |
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- | 0.9867 | 0.4609 | 3600 | 0.9721 | 0.065 |
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- | 0.9194 | 0.4738 | 3700 | 0.9881 | 0.08 |
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- | 1.1577 | 0.4866 | 3800 | 1.1223 | 0.05 |
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- | 0.9286 | 0.4994 | 3900 | 0.9181 | 0.065 |
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- | 0.932 | 0.5122 | 4000 | 0.9695 | 0.035 |
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- | 0.907 | 0.5250 | 4100 | 0.9809 | 0.085 |
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- | 0.8528 | 0.5378 | 4200 | 0.8546 | 0.07 |
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- | 0.8456 | 0.5506 | 4300 | 0.8779 | 0.095 |
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- | 0.7858 | 0.5634 | 4400 | 0.8470 | 0.08 |
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- | 0.8417 | 0.5762 | 4500 | 0.8280 | 0.09 |
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- | 0.8261 | 0.5890 | 4600 | 0.8270 | 0.11 |
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- | 0.8291 | 0.6018 | 4700 | 0.8272 | 0.07 |
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- | 0.782 | 0.6146 | 4800 | 0.7997 | 0.07 |
101
- | 0.7449 | 0.6274 | 4900 | 0.7533 | 0.06 |
102
- | 0.7362 | 0.6402 | 5000 | 0.7722 | 0.1 |
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- | 0.7751 | 0.6530 | 5100 | 0.7441 | 0.11 |
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- | 0.7249 | 0.6658 | 5200 | 0.7591 | 0.08 |
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- | 0.7121 | 0.6786 | 5300 | 0.7160 | 0.17 |
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- | 0.704 | 0.6914 | 5400 | 0.7142 | 0.1 |
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- | 0.6699 | 0.7042 | 5500 | 0.6914 | 0.09 |
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- | 0.6853 | 0.7170 | 5600 | 0.6954 | 0.105 |
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- | 0.6638 | 0.7298 | 5700 | 0.6716 | 0.165 |
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- | 0.6862 | 0.7426 | 5800 | 0.6623 | 0.12 |
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- | 0.655 | 0.7554 | 5900 | 0.6549 | 0.145 |
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- | 0.6251 | 0.7682 | 6000 | 0.6537 | 0.125 |
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- | 0.637 | 0.7810 | 6100 | 0.6379 | 0.155 |
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- | 0.625 | 0.7939 | 6200 | 0.6188 | 0.17 |
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- | 0.6114 | 0.8067 | 6300 | 0.6036 | 0.205 |
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- | 0.6303 | 0.8195 | 6400 | 0.6004 | 0.19 |
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- | 0.5983 | 0.8323 | 6500 | 0.5845 | 0.225 |
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- | 0.6014 | 0.8451 | 6600 | 0.5766 | 0.245 |
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- | 0.5785 | 0.8579 | 6700 | 0.5765 | 0.24 |
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- | 0.5804 | 0.8707 | 6800 | 0.5620 | 0.28 |
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- | 0.5633 | 0.8835 | 6900 | 0.5518 | 0.3 |
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- | 0.5533 | 0.8963 | 7000 | 0.5489 | 0.305 |
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- | 0.5551 | 0.9091 | 7100 | 0.5481 | 0.305 |
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- | 0.569 | 0.9219 | 7200 | 0.5398 | 0.3 |
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- | 0.5583 | 0.9347 | 7300 | 0.5389 | 0.31 |
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- | 0.5357 | 0.9475 | 7400 | 0.5369 | 0.325 |
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- | 0.5453 | 0.9603 | 7500 | 0.5328 | 0.34 |
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- | 0.5472 | 0.9731 | 7600 | 0.5309 | 0.345 |
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- | 0.5349 | 0.9859 | 7700 | 0.5307 | 0.345 |
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- | 0.5309 | 0.9987 | 7800 | 0.5307 | 0.34 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.0033
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+ - Accuracy: 1.0
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22
  ## Model description
23
 
 
47
 
48
  ### Training results
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50
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
51
+ |:-------------:|:------:|:-----:|:---------------:|:--------:|
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+ | No log | 0 | 0 | 2.6145 | 0.0 |
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+ | 2.5809 | 0.0064 | 100 | 2.5687 | 0.0 |
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+ | 2.523 | 0.0128 | 200 | 2.5229 | 0.0 |
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+ | 2.4811 | 0.0192 | 300 | 2.4679 | 0.0 |
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+ | 2.4303 | 0.0256 | 400 | 2.4110 | 0.0 |
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+ | 2.3619 | 0.0320 | 500 | 2.3606 | 0.0 |
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+ | 2.3345 | 0.0384 | 600 | 2.3222 | 0.0 |
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+ | 2.3092 | 0.0448 | 700 | 2.2921 | 0.0 |
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+ | 2.2821 | 0.0512 | 800 | 2.2657 | 0.0 |
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+ | 2.2333 | 0.0576 | 900 | 2.2310 | 0.0 |
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+ | 2.177 | 0.0640 | 1000 | 2.1707 | 0.0 |
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+ | 2.1536 | 0.0704 | 1100 | 2.1321 | 0.0 |
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+ | 2.1367 | 0.0768 | 1200 | 2.0913 | 0.0 |
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+ | 2.0166 | 0.0832 | 1300 | 2.0268 | 0.0 |
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+ | 1.9742 | 0.0896 | 1400 | 2.0546 | 0.0 |
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+ | 1.9483 | 0.0960 | 1500 | 1.9994 | 0.0 |
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+ | 1.8687 | 0.1024 | 1600 | 1.8746 | 0.0 |
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+ | 1.8801 | 0.1088 | 1700 | 1.9225 | 0.0 |
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+ | 1.884 | 0.1152 | 1800 | 1.9165 | 0.0 |
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+ | 1.8477 | 0.1216 | 1900 | 1.7852 | 0.0 |
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+ | 1.8183 | 0.1280 | 2000 | 1.7833 | 0.0 |
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+ | 1.8369 | 0.1344 | 2100 | 1.9553 | 0.0 |
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+ | 1.8137 | 0.1408 | 2200 | 1.7512 | 0.0 |
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+ | 1.6711 | 0.1472 | 2300 | 1.7676 | 0.01 |
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+ | 1.663 | 0.1536 | 2400 | 1.7389 | 0.0 |
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+ | 1.7879 | 0.1600 | 2500 | 1.6750 | 0.005 |
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+ | 1.69 | 0.1664 | 2600 | 1.6753 | 0.005 |
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+ | 1.6681 | 0.1728 | 2700 | 1.7350 | 0.0 |
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+ | 1.7412 | 0.1792 | 2800 | 1.6032 | 0.01 |
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+ | 1.5453 | 0.1856 | 2900 | 1.6210 | 0.005 |
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+ | 1.5741 | 0.1920 | 3000 | 1.6635 | 0.0 |
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+ | 1.5371 | 0.1984 | 3100 | 1.6253 | 0.0 |
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+ | 1.6883 | 0.2048 | 3200 | 1.5333 | 0.005 |
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+ | 1.4715 | 0.2112 | 3300 | 1.6502 | 0.005 |
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+ | 1.4137 | 0.2176 | 3400 | 1.4267 | 0.0 |
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+ | 1.4928 | 0.2240 | 3500 | 1.4612 | 0.0 |
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+ | 1.3538 | 0.2304 | 3600 | 1.3609 | 0.015 |
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+ | 1.341 | 0.2368 | 3700 | 1.3231 | 0.015 |
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+ | 1.3125 | 0.2432 | 3800 | 1.3416 | 0.0 |
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+ | 1.6622 | 0.2496 | 3900 | 1.4710 | 0.005 |
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+ | 1.5242 | 0.2560 | 4000 | 1.4332 | 0.005 |
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+ | 1.2997 | 0.2625 | 4100 | 1.3173 | 0.01 |
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+ | 1.2837 | 0.2689 | 4200 | 1.3149 | 0.005 |
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+ | 1.2307 | 0.2753 | 4300 | 1.1461 | 0.015 |
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+ | 1.5046 | 0.2817 | 4400 | 1.3055 | 0.005 |
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+ | 1.2688 | 0.2881 | 4500 | 1.1161 | 0.01 |
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+ | 1.1872 | 0.2945 | 4600 | 1.1132 | 0.01 |
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+ | 1.1344 | 0.3009 | 4700 | 1.0692 | 0.01 |
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+ | 1.2026 | 0.3073 | 4800 | 1.0552 | 0.0 |
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+ | 1.0938 | 0.3137 | 4900 | 1.0710 | 0.02 |
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+ | 1.0049 | 0.3201 | 5000 | 0.9988 | 0.005 |
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+ | 1.1265 | 0.3265 | 5100 | 0.9553 | 0.03 |
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+ | 0.9829 | 0.3329 | 5200 | 0.9911 | 0.01 |
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+ | 0.9873 | 0.3393 | 5300 | 0.9368 | 0.02 |
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+ | 0.9269 | 0.3457 | 5400 | 0.8815 | 0.02 |
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+ | 0.9027 | 0.3521 | 5500 | 0.9123 | 0.01 |
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+ | 0.8419 | 0.3585 | 5600 | 0.9692 | 0.02 |
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+ | 0.9754 | 0.3649 | 5700 | 0.9221 | 0.04 |
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+ | 0.8729 | 0.3713 | 5800 | 0.9506 | 0.045 |
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+ | 0.7891 | 0.3777 | 5900 | 0.7808 | 0.125 |
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+ | 0.7072 | 0.3841 | 6000 | 0.6781 | 0.17 |
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+ | 0.6546 | 0.3905 | 6100 | 0.6591 | 0.18 |
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+ | 0.5607 | 0.3969 | 6200 | 0.5789 | 0.3 |
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+ | 0.5397 | 0.4033 | 6300 | 0.4997 | 0.445 |
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+ | 0.5981 | 0.4097 | 6400 | 0.4789 | 0.475 |
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+ | 0.4037 | 0.4161 | 6500 | 0.5675 | 0.245 |
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+ | 0.4213 | 0.4225 | 6600 | 0.3815 | 0.63 |
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+ | 0.4639 | 0.4289 | 6700 | 0.3542 | 0.59 |
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+ | 0.3786 | 0.4353 | 6800 | 0.3166 | 0.625 |
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+ | 0.5791 | 0.4417 | 6900 | 0.8131 | 0.13 |
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+ | 0.4567 | 0.4481 | 7000 | 0.2814 | 0.65 |
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+ | 0.4709 | 0.4545 | 7100 | 0.6059 | 0.16 |
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+ | 0.2642 | 0.4609 | 7200 | 0.3014 | 0.53 |
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+ | 0.3518 | 0.4673 | 7300 | 0.2250 | 0.66 |
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+ | 0.2309 | 0.4737 | 7400 | 0.1933 | 0.75 |
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+ | 0.2686 | 0.4801 | 7500 | 0.2457 | 0.54 |
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+ | 0.2142 | 0.4865 | 7600 | 0.2393 | 0.625 |
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+ | 0.1771 | 0.4929 | 7700 | 0.2440 | 0.565 |
130
+ | 0.1637 | 0.4993 | 7800 | 0.1620 | 0.775 |
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+ | 0.2961 | 0.5057 | 7900 | 0.5910 | 0.12 |
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+ | 0.1414 | 0.5121 | 8000 | 0.1640 | 0.74 |
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+ | 0.1106 | 0.5185 | 8100 | 0.1175 | 0.855 |
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+ | 0.1494 | 0.5249 | 8200 | 0.1550 | 0.725 |
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+ | 0.1337 | 0.5313 | 8300 | 0.1139 | 0.85 |
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+ | 0.1713 | 0.5377 | 8400 | 0.1009 | 0.86 |
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+ | 0.1294 | 0.5441 | 8500 | 0.1391 | 0.755 |
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+ | 0.1582 | 0.5505 | 8600 | 0.0950 | 0.86 |
139
+ | 0.0931 | 0.5569 | 8700 | 0.0985 | 0.845 |
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+ | 0.0663 | 0.5633 | 8800 | 0.1735 | 0.635 |
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+ | 0.1151 | 0.5697 | 8900 | 0.1516 | 0.69 |
142
+ | 0.1891 | 0.5761 | 9000 | 0.0983 | 0.8 |
143
+ | 0.1057 | 0.5825 | 9100 | 0.0902 | 0.85 |
144
+ | 0.1255 | 0.5889 | 9200 | 0.0935 | 0.825 |
145
+ | 0.1474 | 0.5953 | 9300 | 0.0715 | 0.89 |
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+ | 0.1108 | 0.6017 | 9400 | 0.1197 | 0.78 |
147
+ | 0.1694 | 0.6081 | 9500 | 0.2394 | 0.485 |
148
+ | 0.0989 | 0.6145 | 9600 | 0.0985 | 0.83 |
149
+ | 0.1155 | 0.6209 | 9700 | 0.0745 | 0.88 |
150
+ | 0.2256 | 0.6273 | 9800 | 0.1757 | 0.63 |
151
+ | 0.1155 | 0.6337 | 9900 | 0.1612 | 0.6 |
152
+ | 0.0529 | 0.6401 | 10000 | 0.0762 | 0.85 |
153
+ | 0.0928 | 0.6465 | 10100 | 0.0647 | 0.875 |
154
+ | 0.0858 | 0.6529 | 10200 | 0.1147 | 0.735 |
155
+ | 0.0486 | 0.6593 | 10300 | 0.0699 | 0.85 |
156
+ | 0.1232 | 0.6657 | 10400 | 0.0697 | 0.87 |
157
+ | 0.0504 | 0.6721 | 10500 | 0.0576 | 0.9 |
158
+ | 0.0307 | 0.6785 | 10600 | 0.0409 | 0.935 |
159
+ | 0.0489 | 0.6849 | 10700 | 0.0815 | 0.835 |
160
+ | 0.0388 | 0.6913 | 10800 | 0.0256 | 0.97 |
161
+ | 0.0296 | 0.6977 | 10900 | 0.0586 | 0.865 |
162
+ | 0.0444 | 0.7041 | 11000 | 0.0278 | 0.96 |
163
+ | 0.0251 | 0.7105 | 11100 | 0.0280 | 0.95 |
164
+ | 0.0489 | 0.7169 | 11200 | 0.0504 | 0.895 |
165
+ | 0.0264 | 0.7233 | 11300 | 0.0315 | 0.945 |
166
+ | 0.0293 | 0.7297 | 11400 | 0.0254 | 0.955 |
167
+ | 0.0143 | 0.7361 | 11500 | 0.0211 | 0.955 |
168
+ | 0.0288 | 0.7425 | 11600 | 0.0614 | 0.855 |
169
+ | 0.0278 | 0.7489 | 11700 | 0.0228 | 0.965 |
170
+ | 0.034 | 0.7553 | 11800 | 0.0175 | 0.975 |
171
+ | 0.0408 | 0.7617 | 11900 | 0.0374 | 0.93 |
172
+ | 0.0255 | 0.7681 | 12000 | 0.0453 | 0.9 |
173
+ | 0.0175 | 0.7745 | 12100 | 0.0229 | 0.965 |
174
+ | 0.014 | 0.7809 | 12200 | 0.0112 | 0.995 |
175
+ | 0.0213 | 0.7874 | 12300 | 0.0238 | 0.965 |
176
+ | 0.0082 | 0.7938 | 12400 | 0.0110 | 0.985 |
177
+ | 0.0211 | 0.8002 | 12500 | 0.0120 | 0.985 |
178
+ | 0.0111 | 0.8066 | 12600 | 0.0117 | 0.98 |
179
+ | 0.0074 | 0.8130 | 12700 | 0.0136 | 0.965 |
180
+ | 0.0108 | 0.8194 | 12800 | 0.0083 | 0.995 |
181
+ | 0.013 | 0.8258 | 12900 | 0.0098 | 0.99 |
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+ | 0.0076 | 0.8322 | 13000 | 0.0074 | 0.995 |
183
+ | 0.0084 | 0.8386 | 13100 | 0.0106 | 0.98 |
184
+ | 0.0119 | 0.8450 | 13200 | 0.0068 | 0.995 |
185
+ | 0.0059 | 0.8514 | 13300 | 0.0079 | 0.98 |
186
+ | 0.0064 | 0.8578 | 13400 | 0.0067 | 0.99 |
187
+ | 0.0048 | 0.8642 | 13500 | 0.0059 | 0.995 |
188
+ | 0.0043 | 0.8706 | 13600 | 0.0044 | 1.0 |
189
+ | 0.007 | 0.8770 | 13700 | 0.0088 | 0.985 |
190
+ | 0.0043 | 0.8834 | 13800 | 0.0042 | 1.0 |
191
+ | 0.003 | 0.8898 | 13900 | 0.0060 | 0.995 |
192
+ | 0.0037 | 0.8962 | 14000 | 0.0052 | 0.99 |
193
+ | 0.0064 | 0.9026 | 14100 | 0.0089 | 0.985 |
194
+ | 0.0029 | 0.9090 | 14200 | 0.0039 | 1.0 |
195
+ | 0.0054 | 0.9154 | 14300 | 0.0037 | 1.0 |
196
+ | 0.0031 | 0.9218 | 14400 | 0.0037 | 1.0 |
197
+ | 0.0031 | 0.9282 | 14500 | 0.0035 | 1.0 |
198
+ | 0.0039 | 0.9346 | 14600 | 0.0036 | 1.0 |
199
+ | 0.0028 | 0.9410 | 14700 | 0.0039 | 1.0 |
200
+ | 0.0027 | 0.9474 | 14800 | 0.0033 | 1.0 |
201
+ | 0.0027 | 0.9538 | 14900 | 0.0031 | 1.0 |
202
+ | 0.0037 | 0.9602 | 15000 | 0.0032 | 1.0 |
203
+ | 0.0026 | 0.9666 | 15100 | 0.0031 | 1.0 |
204
+ | 0.0025 | 0.9730 | 15200 | 0.0033 | 1.0 |
205
+ | 0.0027 | 0.9794 | 15300 | 0.0031 | 1.0 |
206
+ | 0.0033 | 0.9858 | 15400 | 0.0034 | 1.0 |
207
+ | 0.0025 | 0.9922 | 15500 | 0.0033 | 1.0 |
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+ | 0.0025 | 0.9986 | 15600 | 0.0033 | 1.0 |
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  ### Framework versions
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