generated_from_trainer

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gpt-expt-sp

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss
3.8623 3.12 100 1.7653
1.6403 6.24 200 1.5635
1.5806 9.37 300 1.5326
1.5433 12.49 400 1.4568
1.362 15.61 500 0.9368
0.8739 18.73 600 0.5006
0.5905 21.85 700 0.3875
0.4755 24.98 800 0.3440
0.4252 28.12 900 0.3238
0.3904 31.24 1000 0.3093
0.366 34.37 1100 0.3004
0.3492 37.49 1200 0.2922
0.3345 40.61 1300 0.2860
0.3277 43.73 1400 0.2819
0.324 46.85 1500 0.2800
0.318 49.98 1600 0.2766
0.314 53.12 1700 0.2736
0.308 56.24 1800 0.2740
0.306 59.37 1900 0.2716
0.3037 62.49 2000 0.2708
0.2993 65.61 2100 0.2685
0.2991 68.73 2200 0.2680
0.297 71.85 2300 0.2670
0.2964 74.98 2400 0.2662
0.2964 78.12 2500 0.2653
0.2942 81.24 2600 0.2664
0.2937 84.37 2700 0.2655
0.2886 87.49 2800 0.2631
0.2877 90.61 2900 0.2634
0.2859 93.73 3000 0.2628
0.2852 96.85 3100 0.2629
0.2841 99.98 3200 0.2629
0.2848 103.12 3300 0.2625
0.2811 106.24 3400 0.2611
0.281 109.37 3500 0.2608
0.2794 112.49 3600 0.2599
0.2787 115.61 3700 0.2604
0.2781 118.73 3800 0.2601
0.2777 121.85 3900 0.2604
0.2776 124.98 4000 0.2600
0.2786 128.12 4100 0.2597
0.2757 131.24 4200 0.2597
0.2754 134.37 4300 0.2590
0.2758 137.49 4400 0.2596
0.2742 140.61 4500 0.2598
0.2731 143.73 4600 0.2581
0.2738 146.85 4700 0.2587
0.273 149.98 4800 0.2583
0.2736 153.12 4900 0.2579
0.271 156.24 5000 0.2580
0.2709 159.37 5100 0.2578
0.2708 162.49 5200 0.2582
0.2697 165.61 5300 0.2578
0.2695 168.73 5400 0.2578
0.269 171.85 5500 0.2582
0.2691 174.98 5600 0.2574
0.2705 178.12 5700 0.2574
0.2678 181.24 5800 0.2572
0.2692 184.37 5900 0.2582
0.2687 187.49 6000 0.2572
0.2673 190.61 6100 0.2571
0.2666 193.73 6200 0.2568
0.2662 196.85 6300 0.2573
0.2662 199.98 6400 0.2568
0.2688 203.12 6500 0.2567
0.2658 206.24 6600 0.2570
0.2666 209.37 6700 0.2567
0.2652 212.49 6800 0.2565
0.2651 215.61 6900 0.2568
0.2649 218.73 7000 0.2566
0.2648 221.85 7100 0.2564
0.2645 224.98 7200 0.2564
0.2662 228.12 7300 0.2564
0.2641 231.24 7400 0.2564
0.2641 234.37 7500 0.2563
0.2639 237.49 7600 0.2563
0.2638 240.61 7700 0.2563
0.2637 243.73 7800 0.2562
0.2635 246.85 7900 0.2562
0.2633 249.98 8000 0.2563
0.2653 253.12 8100 0.2562
0.2631 256.24 8200 0.2562
0.2631 259.37 8300 0.2561
0.263 262.49 8400 0.2561
0.263 265.61 8500 0.2561
0.2629 268.73 8600 0.2561
0.2628 271.85 8700 0.2561
0.2628 274.98 8800 0.2561
0.2646 278.12 8900 0.2561
0.2626 281.24 9000 0.2561
0.2626 284.37 9100 0.2561
0.2625 287.49 9200 0.2561
0.2626 290.61 9300 0.2561
0.2626 293.73 9400 0.2561
0.2626 296.85 9500 0.2561
0.2625 299.98 9600 0.2561

Framework versions