generated_from_trainer

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jpqd-bert-base-ft-sst2

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.

It was compressed with NNCF following the Optimum JPQD text-classification example

It achieves the following results on the evaluation set:

Training hyperparameters

The following hyperparameters were used during training:

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.392 0.12 250 0.4535 0.8888
0.4413 0.24 500 0.4671 0.8899
0.29 0.36 750 0.3285 0.9128
0.2851 0.48 1000 0.2498 0.9151
0.3717 0.59 1250 0.2037 0.9243
0.2467 0.71 1500 0.2840 0.9174
0.2114 0.83 1750 0.2239 0.9243
0.1777 0.95 2000 0.1968 0.9266
2.6501 1.07 2250 2.8219 0.9255
6.4768 1.19 2500 6.5765 0.8979
9.3594 1.31 2750 9.4648 0.8819
11.5481 1.43 3000 11.5391 0.8567
12.7541 1.54 3250 12.8359 0.8578
13.6184 1.66 3500 13.6519 0.8429
13.9171 1.78 3750 14.0734 0.8475
13.9601 1.9 4000 14.1024 0.8578
0.2701 2.02 4250 0.3354 0.9048
0.2689 2.14 4500 0.3320 0.9048
0.1775 2.26 4750 0.2838 0.9163
0.1648 2.38 5000 0.2842 0.9128
0.1316 2.49 5250 0.2750 0.9163
0.2349 2.61 5500 0.2405 0.9232
0.066 2.73 5750 0.2695 0.9174
0.1285 2.85 6000 0.3017 0.9094
0.1813 2.97 6250 0.3472 0.9106
0.078 3.09 6500 0.2915 0.9140
0.0886 3.21 6750 0.2853 0.9151
0.117 3.33 7000 0.2689 0.9186
0.0894 3.44 7250 0.2748 0.9174
0.1023 3.56 7500 0.3279 0.9094
0.0495 3.68 7750 0.2988 0.9151
0.0899 3.8 8000 0.2796 0.9174
0.1102 3.92 8250 0.2667 0.9163
0.061 4.04 8500 0.2837 0.9174
0.0594 4.16 8750 0.2766 0.9151
0.1062 4.28 9000 0.2777 0.9140
0.0751 4.39 9250 0.2690 0.9220
0.0386 4.51 9500 0.2668 0.9163
0.0284 4.63 9750 0.2812 0.9186
0.1016 4.75 10000 0.2825 0.9163
0.0507 4.87 10250 0.2805 0.9140
0.0709 4.99 10500 0.2855 0.9140

Framework versions