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lge_tests_prelim

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Safetensors
nanogpt
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  1. README.md +81 -81
  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: 0.8346
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- - Accuracy: 0.0
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  ## Model description
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@@ -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 | 2.6609 | 0.0 |
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- | 1.8563 | 0.0128 | 100 | 1.8701 | 0.0 |
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- | 1.8631 | 0.0256 | 200 | 1.8579 | 0.0 |
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- | 1.8648 | 0.0384 | 300 | 1.8517 | 0.0 |
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- | 1.8491 | 0.0512 | 400 | 1.8521 | 0.0 |
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- | 1.8554 | 0.0640 | 500 | 1.8477 | 0.0 |
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- | 1.8473 | 0.0768 | 600 | 1.8471 | 0.0 |
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- | 1.8067 | 0.0896 | 700 | 1.7959 | 0.0 |
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- | 1.6826 | 0.1024 | 800 | 1.6881 | 0.0 |
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- | 1.7295 | 0.1152 | 900 | 1.7043 | 0.0 |
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- | 1.5475 | 0.1280 | 1000 | 1.5749 | 0.0 |
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- | 1.5244 | 0.1408 | 1100 | 1.5619 | 0.0 |
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- | 1.5361 | 0.1536 | 1200 | 1.5511 | 0.0 |
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- | 1.4774 | 0.1665 | 1300 | 1.5394 | 0.0 |
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- | 1.4691 | 0.1793 | 1400 | 1.5045 | 0.0 |
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- | 1.4446 | 0.1921 | 1500 | 1.3720 | 0.0 |
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- | 1.2363 | 0.2049 | 1600 | 1.2546 | 0.0 |
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- | 1.3268 | 0.2177 | 1700 | 1.2886 | 0.0 |
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- | 1.2509 | 0.2305 | 1800 | 1.2707 | 0.0 |
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- | 1.3544 | 0.2433 | 1900 | 1.2846 | 0.0 |
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- | 1.2432 | 0.2561 | 2000 | 1.2700 | 0.0 |
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- | 1.1864 | 0.2689 | 2100 | 1.2224 | 0.0 |
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- | 1.3889 | 0.2817 | 2200 | 1.4080 | 0.0 |
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- | 1.1777 | 0.2945 | 2300 | 1.2035 | 0.0 |
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- | 1.2342 | 0.3073 | 2400 | 1.1984 | 0.0 |
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- | 1.1624 | 0.3201 | 2500 | 1.1610 | 0.0 |
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- | 1.1352 | 0.3329 | 2600 | 1.1979 | 0.0 |
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- | 1.2747 | 0.3457 | 2700 | 1.2184 | 0.0 |
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- | 1.1499 | 0.3585 | 2800 | 1.1461 | 0.0 |
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- | 1.086 | 0.3713 | 2900 | 1.1256 | 0.0 |
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- | 1.0623 | 0.3841 | 3000 | 1.1108 | 0.0 |
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- | 1.14 | 0.3969 | 3100 | 1.1380 | 0.0 |
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- | 1.092 | 0.4097 | 3200 | 1.1087 | 0.0 |
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- | 1.1119 | 0.4225 | 3300 | 1.1128 | 0.0 |
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- | 1.0986 | 0.4353 | 3400 | 1.1065 | 0.0 |
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- | 1.1209 | 0.4481 | 3500 | 1.1146 | 0.0 |
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- | 1.1403 | 0.4609 | 3600 | 1.1177 | 0.0 |
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- | 1.0833 | 0.4738 | 3700 | 1.1079 | 0.0 |
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- | 1.058 | 0.4866 | 3800 | 1.0979 | 0.0 |
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- | 1.0343 | 0.4994 | 3900 | 1.0948 | 0.0 |
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- | 1.114 | 0.5122 | 4000 | 1.0905 | 0.0 |
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- | 1.0575 | 0.5250 | 4100 | 1.0938 | 0.0 |
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- | 1.0455 | 0.5378 | 4200 | 1.0924 | 0.0 |
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- | 1.1491 | 0.5506 | 4300 | 1.0869 | 0.0 |
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- | 1.0754 | 0.5634 | 4400 | 1.0866 | 0.0 |
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- | 1.1082 | 0.5762 | 4500 | 1.0741 | 0.0 |
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- | 1.068 | 0.5890 | 4600 | 1.0692 | 0.0 |
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- | 1.0465 | 0.6018 | 4700 | 1.0755 | 0.0 |
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- | 1.1529 | 0.6146 | 4800 | 1.1614 | 0.0 |
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- | 0.9963 | 0.6274 | 4900 | 1.0647 | 0.01 |
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- | 1.0269 | 0.6402 | 5000 | 1.0322 | 0.0 |
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- | 0.9824 | 0.6530 | 5100 | 1.0241 | 0.0 |
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- | 0.989 | 0.6658 | 5200 | 1.0183 | 0.0 |
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- | 1.0653 | 0.6786 | 5300 | 0.9991 | 0.005 |
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- | 0.9786 | 0.6914 | 5400 | 1.0060 | 0.0 |
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- | 1.1023 | 0.7042 | 5500 | 1.0032 | 0.0 |
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- | 0.9057 | 0.7170 | 5600 | 0.9354 | 0.0 |
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- | 0.9622 | 0.7298 | 5700 | 0.9240 | 0.0 |
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- | 0.8582 | 0.7426 | 5800 | 0.9358 | 0.0 |
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- | 0.8251 | 0.7554 | 5900 | 0.8959 | 0.005 |
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- | 0.7712 | 0.7682 | 6000 | 0.8940 | 0.005 |
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- | 0.8207 | 0.7810 | 6100 | 0.8885 | 0.005 |
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- | 0.8718 | 0.7939 | 6200 | 0.8886 | 0.0 |
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- | 0.8238 | 0.8067 | 6300 | 0.8771 | 0.0 |
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- | 0.9613 | 0.8195 | 6400 | 0.8826 | 0.0 |
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- | 0.821 | 0.8323 | 6500 | 0.8762 | 0.0 |
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- | 0.847 | 0.8451 | 6600 | 0.8555 | 0.01 |
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- | 0.9417 | 0.8579 | 6700 | 0.8519 | 0.0 |
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- | 0.808 | 0.8707 | 6800 | 0.8491 | 0.0 |
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- | 0.9 | 0.8835 | 6900 | 0.8519 | 0.0 |
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- | 0.8663 | 0.8963 | 7000 | 0.8457 | 0.0 |
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- | 0.844 | 0.9091 | 7100 | 0.8448 | 0.005 |
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- | 0.9081 | 0.9219 | 7200 | 0.8374 | 0.0 |
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- | 0.7387 | 0.9347 | 7300 | 0.8376 | 0.0 |
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- | 0.8548 | 0.9475 | 7400 | 0.8345 | 0.0 |
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- | 0.8636 | 0.9603 | 7500 | 0.8345 | 0.0 |
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- | 0.8227 | 0.9731 | 7600 | 0.8345 | 0.0 |
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- | 0.7796 | 0.9859 | 7700 | 0.8347 | 0.0 |
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- | 0.8433 | 0.9987 | 7800 | 0.8346 | 0.0 |
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  ### Framework versions
 
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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: 0.1683
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+ - Accuracy: 0.82
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | No log | 0 | 0 | 2.6169 | 0.0 |
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+ | 1.6014 | 0.0128 | 100 | 1.5596 | 0.0 |
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+ | 1.5621 | 0.0256 | 200 | 1.5474 | 0.0 |
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+ | 1.473 | 0.0384 | 300 | 1.4950 | 0.0 |
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+ | 1.0233 | 0.0512 | 400 | 1.0690 | 0.075 |
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+ | 0.3934 | 0.0640 | 500 | 0.5094 | 0.5 |
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+ | 0.587 | 0.0768 | 600 | 0.5615 | 0.375 |
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+ | 0.3837 | 0.0896 | 700 | 0.4922 | 0.59 |
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+ | 0.376 | 0.1024 | 800 | 0.4058 | 0.645 |
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+ | 0.455 | 0.1152 | 900 | 0.3661 | 0.76 |
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+ | 0.4562 | 0.1280 | 1000 | 0.3777 | 0.635 |
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+ | 0.2314 | 0.1408 | 1100 | 0.3058 | 0.785 |
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+ | 0.6504 | 0.1536 | 1200 | 0.3527 | 0.72 |
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+ | 0.4729 | 0.1665 | 1300 | 0.6792 | 0.395 |
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+ | 0.3412 | 0.1793 | 1400 | 0.3194 | 0.775 |
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+ | 0.4038 | 0.1921 | 1500 | 0.4074 | 0.645 |
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+ | 0.3973 | 0.2049 | 1600 | 0.3489 | 0.77 |
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+ | 0.4487 | 0.2177 | 1700 | 0.3055 | 0.79 |
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+ | 0.3079 | 0.2305 | 1800 | 0.3047 | 0.81 |
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+ | 0.3932 | 0.2433 | 1900 | 0.3322 | 0.735 |
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+ | 0.5617 | 0.2561 | 2000 | 0.4301 | 0.615 |
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+ | 0.588 | 0.2689 | 2100 | 0.4464 | 0.585 |
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+ | 0.2426 | 0.2817 | 2200 | 0.2919 | 0.815 |
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+ | 0.4484 | 0.2945 | 2300 | 0.3360 | 0.795 |
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+ | 0.3399 | 0.3073 | 2400 | 0.3569 | 0.755 |
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+ | 0.182 | 0.3201 | 2500 | 0.3048 | 0.79 |
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+ | 0.2704 | 0.3329 | 2600 | 0.3224 | 0.75 |
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+ | 0.3972 | 0.3457 | 2700 | 0.2751 | 0.815 |
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+ | 0.3842 | 0.3585 | 2800 | 0.2936 | 0.79 |
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+ | 0.2323 | 0.3713 | 2900 | 0.2895 | 0.76 |
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+ | 0.2711 | 0.3841 | 3000 | 0.3073 | 0.78 |
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+ | 0.4402 | 0.3969 | 3100 | 0.4514 | 0.65 |
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+ | 0.3037 | 0.4097 | 3200 | 0.2986 | 0.775 |
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+ | 0.2244 | 0.4225 | 3300 | 0.2810 | 0.755 |
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+ | 0.3728 | 0.4353 | 3400 | 0.4621 | 0.615 |
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+ | 0.3927 | 0.4481 | 3500 | 0.4407 | 0.585 |
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+ | 0.3207 | 0.4609 | 3600 | 0.3104 | 0.765 |
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+ | 0.1441 | 0.4738 | 3700 | 0.2570 | 0.805 |
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+ | 0.3894 | 0.4866 | 3800 | 0.2745 | 0.74 |
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+ | 0.1879 | 0.4994 | 3900 | 0.2391 | 0.81 |
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+ | 0.1923 | 0.5122 | 4000 | 0.2407 | 0.785 |
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+ | 0.2663 | 0.5250 | 4100 | 0.2757 | 0.77 |
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+ | 0.3024 | 0.5378 | 4200 | 0.2855 | 0.75 |
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+ | 0.4844 | 0.5506 | 4300 | 0.4087 | 0.54 |
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+ | 0.2889 | 0.5634 | 4400 | 0.2994 | 0.675 |
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+ | 0.3159 | 0.5762 | 4500 | 0.2682 | 0.75 |
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+ | 0.1506 | 0.5890 | 4600 | 0.2356 | 0.785 |
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+ | 0.1468 | 0.6018 | 4700 | 0.2175 | 0.805 |
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+ | 0.2704 | 0.6146 | 4800 | 0.2180 | 0.785 |
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+ | 0.2008 | 0.6274 | 4900 | 0.2341 | 0.785 |
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+ | 0.2038 | 0.6402 | 5000 | 0.2490 | 0.765 |
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+ | 0.1946 | 0.6530 | 5100 | 0.2232 | 0.81 |
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+ | 0.1762 | 0.6658 | 5200 | 0.2150 | 0.805 |
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+ | 0.1857 | 0.6786 | 5300 | 0.1878 | 0.815 |
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+ | 0.1379 | 0.6914 | 5400 | 0.1841 | 0.815 |
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+ | 0.2637 | 0.7042 | 5500 | 0.1939 | 0.82 |
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+ | 0.2117 | 0.7170 | 5600 | 0.1981 | 0.81 |
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+ | 0.2847 | 0.7298 | 5700 | 0.1896 | 0.8 |
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+ | 0.1545 | 0.7426 | 5800 | 0.1909 | 0.8 |
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+ | 0.2638 | 0.7554 | 5900 | 0.1953 | 0.805 |
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+ | 0.1393 | 0.7682 | 6000 | 0.1827 | 0.815 |
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+ | 0.2409 | 0.7810 | 6100 | 0.1782 | 0.81 |
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+ | 0.1625 | 0.7939 | 6200 | 0.1797 | 0.81 |
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+ | 0.2336 | 0.8067 | 6300 | 0.1749 | 0.82 |
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+ | 0.1545 | 0.8195 | 6400 | 0.1781 | 0.82 |
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+ | 0.144 | 0.8323 | 6500 | 0.1710 | 0.82 |
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+ | 0.1765 | 0.8451 | 6600 | 0.1712 | 0.825 |
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+ | 0.2032 | 0.8579 | 6700 | 0.1722 | 0.815 |
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+ | 0.298 | 0.8707 | 6800 | 0.1726 | 0.825 |
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+ | 0.1353 | 0.8835 | 6900 | 0.1701 | 0.82 |
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+ | 0.1949 | 0.8963 | 7000 | 0.1690 | 0.82 |
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+ | 0.1645 | 0.9091 | 7100 | 0.1706 | 0.825 |
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+ | 0.1784 | 0.9219 | 7200 | 0.1691 | 0.825 |
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+ | 0.2072 | 0.9347 | 7300 | 0.1675 | 0.82 |
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+ | 0.1874 | 0.9475 | 7400 | 0.1678 | 0.82 |
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+ | 0.0968 | 0.9603 | 7500 | 0.1679 | 0.82 |
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+ | 0.1159 | 0.9731 | 7600 | 0.1683 | 0.82 |
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+ | 0.2488 | 0.9859 | 7700 | 0.1683 | 0.82 |
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+ | 0.1224 | 0.9987 | 7800 | 0.1683 | 0.82 |
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  ### Framework versions
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@@ -1,3 +1,3 @@
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