--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: GPT-2_para3M_512 results: [] --- # GPT-2_para3M_2epoch_256 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1100 ## 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: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.1873 | 0.01 | 500 | 4.0187 | | 3.5461 | 0.02 | 1000 | 3.4287 | | 3.2706 | 0.04 | 1500 | 3.1495 | | 3.105 | 0.05 | 2000 | 2.9773 | | 2.9885 | 0.06 | 2500 | 2.8566 | | 2.8931 | 0.07 | 3000 | 2.7720 | | 2.8307 | 0.08 | 3500 | 2.7016 | | 2.7912 | 0.09 | 4000 | 2.6474 | | 2.7295 | 0.11 | 4500 | 2.5972 | | 2.6927 | 0.12 | 5000 | 2.5641 | | 2.6756 | 0.13 | 5500 | 2.5248 | | 2.6536 | 0.14 | 6000 | 2.4972 | | 2.6186 | 0.15 | 6500 | 2.4730 | | 2.5947 | 0.17 | 7000 | 2.4492 | | 2.591 | 0.18 | 7500 | 2.4313 | | 2.5706 | 0.19 | 8000 | 2.4172 | | 2.5441 | 0.2 | 8500 | 2.3991 | | 2.5266 | 0.21 | 9000 | 2.3838 | | 2.5259 | 0.22 | 9500 | 2.3740 | | 2.5173 | 0.24 | 10000 | 2.3629 | | 2.5122 | 0.25 | 10500 | 2.3549 | | 2.5004 | 0.26 | 11000 | 2.3409 | | 2.4902 | 0.27 | 11500 | 2.3364 | | 2.4735 | 0.28 | 12000 | 2.3242 | | 2.4784 | 0.29 | 12500 | 2.3193 | | 2.4754 | 0.31 | 13000 | 2.3126 | | 2.4587 | 0.32 | 13500 | 2.3077 | | 2.4613 | 0.33 | 14000 | 2.3050 | | 2.4562 | 0.34 | 14500 | 2.2968 | | 2.4422 | 0.35 | 15000 | 2.2913 | | 2.4307 | 0.37 | 15500 | 2.2870 | | 2.4339 | 0.38 | 16000 | 2.2814 | | 2.445 | 0.39 | 16500 | 2.2801 | | 2.4257 | 0.4 | 17000 | 2.2747 | | 2.425 | 0.41 | 17500 | 2.2709 | | 2.4095 | 0.42 | 18000 | 2.2672 | | 2.4137 | 0.44 | 18500 | 2.2632 | | 2.4284 | 0.45 | 19000 | 2.2601 | | 2.419 | 0.46 | 19500 | 2.2569 | | 2.4221 | 0.47 | 20000 | 2.2504 | | 2.3951 | 0.48 | 20500 | 2.2507 | | 2.4054 | 0.5 | 21000 | 2.2515 | | 2.3977 | 0.51 | 21500 | 2.2442 | | 2.4009 | 0.52 | 22000 | 2.2422 | | 2.3941 | 0.53 | 22500 | 2.2388 | | 2.3909 | 0.54 | 23000 | 2.2349 | | 2.4016 | 0.55 | 23500 | 2.2380 | | 2.389 | 0.57 | 24000 | 2.2326 | | 2.3864 | 0.58 | 24500 | 2.2287 | | 2.3795 | 0.59 | 25000 | 2.2285 | | 2.3817 | 0.6 | 25500 | 2.2266 | | 2.3789 | 0.61 | 26000 | 2.2256 | | 2.3801 | 0.62 | 26500 | 2.2210 | | 2.3687 | 0.64 | 27000 | 2.2189 | | 2.378 | 0.65 | 27500 | 2.2194 | | 2.3735 | 0.66 | 28000 | 2.2157 | | 2.3758 | 0.67 | 28500 | 2.2142 | | 2.3616 | 0.68 | 29000 | 2.2133 | | 2.3731 | 0.7 | 29500 | 2.2085 | | 2.3606 | 0.71 | 30000 | 2.2115 | | 2.3516 | 0.72 | 30500 | 2.2072 | | 2.3551 | 0.73 | 31000 | 2.2067 | | 2.3626 | 0.74 | 31500 | 2.2033 | | 2.3516 | 0.75 | 32000 | 2.2031 | | 2.3658 | 0.77 | 32500 | 2.2008 | | 2.3554 | 0.78 | 33000 | 2.1992 | | 2.3524 | 0.79 | 33500 | 2.1988 | | 2.3509 | 0.8 | 34000 | 2.1996 | | 2.3474 | 0.81 | 34500 | 2.1949 | | 2.3431 | 0.83 | 35000 | 2.1943 | | 2.3413 | 0.84 | 35500 | 2.1907 | | 2.3592 | 0.85 | 36000 | 2.1917 | | 2.3636 | 0.86 | 36500 | 2.1919 | | 2.3529 | 0.87 | 37000 | 2.1881 | | 2.3371 | 0.88 | 37500 | 2.1875 | | 2.3413 | 0.9 | 38000 | 2.1856 | | 2.3463 | 0.91 | 38500 | 2.1839 | | 2.3303 | 0.92 | 39000 | 2.1859 | | 2.3432 | 0.93 | 39500 | 2.1790 | | 2.3455 | 0.94 | 40000 | 2.1801 | | 2.344 | 0.95 | 40500 | 2.1761 | | 2.3442 | 0.97 | 41000 | 2.1759 | | 2.3331 | 0.98 | 41500 | 2.1760 | | 2.3391 | 0.99 | 42000 | 2.1748 | | 2.3275 | 1.0 | 42500 | 2.1760 | | 2.3308 | 1.01 | 43000 | 2.1712 | | 2.3191 | 1.03 | 43500 | 2.1727 | | 2.3182 | 1.04 | 44000 | 2.1682 | | 2.3184 | 1.05 | 44500 | 2.1683 | | 2.3177 | 1.06 | 45000 | 2.1668 | | 2.3163 | 1.07 | 45500 | 2.1643 | | 2.321 | 1.08 | 46000 | 2.1631 | | 2.3164 | 1.1 | 46500 | 2.1655 | | 2.3231 | 1.11 | 47000 | 2.1631 | | 2.3139 | 1.12 | 47500 | 2.1591 | | 2.3223 | 1.13 | 48000 | 2.1588 | | 2.3133 | 1.14 | 48500 | 2.1588 | | 2.2995 | 1.16 | 49000 | 2.1569 | | 2.308 | 1.17 | 49500 | 2.1578 | | 2.3062 | 1.18 | 50000 | 2.1539 | | 2.3203 | 1.19 | 50500 | 2.1538 | | 2.3116 | 1.2 | 51000 | 2.1526 | | 2.294 | 1.21 | 51500 | 2.1520 | | 2.2941 | 1.23 | 52000 | 2.1499 | | 2.3053 | 1.24 | 52500 | 2.1502 | | 2.3154 | 1.25 | 53000 | 2.1507 | | 2.3057 | 1.26 | 53500 | 2.1485 | | 2.3106 | 1.27 | 54000 | 2.1464 | | 2.3035 | 1.28 | 54500 | 2.1457 | | 2.304 | 1.3 | 55000 | 2.1445 | | 2.2985 | 1.31 | 55500 | 2.1439 | | 2.296 | 1.32 | 56000 | 2.1421 | | 2.2917 | 1.33 | 56500 | 2.1411 | | 2.2936 | 1.34 | 57000 | 2.1406 | | 2.2866 | 1.36 | 57500 | 2.1383 | | 2.2973 | 1.37 | 58000 | 2.1396 | | 2.2865 | 1.38 | 58500 | 2.1378 | | 2.2929 | 1.39 | 59000 | 2.1370 | | 2.2858 | 1.4 | 59500 | 2.1351 | | 2.2857 | 1.41 | 60000 | 2.1350 | | 2.3019 | 1.43 | 60500 | 2.1338 | | 2.289 | 1.44 | 61000 | 2.1330 | | 2.2874 | 1.45 | 61500 | 2.1318 | | 2.2858 | 1.46 | 62000 | 2.1305 | | 2.2875 | 1.47 | 62500 | 2.1298 | | 2.2859 | 1.49 | 63000 | 2.1294 | | 2.28 | 1.5 | 63500 | 2.1275 | | 2.2866 | 1.51 | 64000 | 2.1277 | | 2.2851 | 1.52 | 64500 | 2.1281 | | 2.2806 | 1.53 | 65000 | 2.1258 | | 2.2889 | 1.54 | 65500 | 2.1245 | | 2.2745 | 1.56 | 66000 | 2.1249 | | 2.2739 | 1.57 | 66500 | 2.1230 | | 2.2853 | 1.58 | 67000 | 2.1226 | | 2.2773 | 1.59 | 67500 | 2.1228 | | 2.2742 | 1.6 | 68000 | 2.1214 | | 2.2656 | 1.61 | 68500 | 2.1200 | | 2.2756 | 1.63 | 69000 | 2.1194 | | 2.2806 | 1.64 | 69500 | 2.1193 | | 2.271 | 1.65 | 70000 | 2.1186 | | 2.2671 | 1.66 | 70500 | 2.1185 | | 2.2718 | 1.67 | 71000 | 2.1168 | | 2.2781 | 1.69 | 71500 | 2.1172 | | 2.2744 | 1.7 | 72000 | 2.1164 | | 2.2744 | 1.71 | 72500 | 2.1156 | | 2.2603 | 1.72 | 73000 | 2.1154 | | 2.2703 | 1.73 | 73500 | 2.1141 | | 2.267 | 1.74 | 74000 | 2.1141 | | 2.2614 | 1.76 | 74500 | 2.1141 | | 2.263 | 1.77 | 75000 | 2.1133 | | 2.2668 | 1.78 | 75500 | 2.1128 | | 2.2642 | 1.79 | 76000 | 2.1128 | | 2.2637 | 1.8 | 76500 | 2.1128 | | 2.2692 | 1.82 | 77000 | 2.1118 | | 2.2631 | 1.83 | 77500 | 2.1117 | | 2.2567 | 1.84 | 78000 | 2.1116 | | 2.2707 | 1.85 | 78500 | 2.1112 | | 2.2707 | 1.86 | 79000 | 2.1109 | | 2.2664 | 1.87 | 79500 | 2.1114 | | 2.266 | 1.89 | 80000 | 2.1113 | | 2.2645 | 1.9 | 80500 | 2.1108 | | 2.2767 | 1.91 | 81000 | 2.1106 | | 2.274 | 1.92 | 81500 | 2.1102 | | 2.2587 | 1.93 | 82000 | 2.1102 | | 2.2736 | 1.94 | 82500 | 2.1100 | | 2.2633 | 1.96 | 83000 | 2.1102 | | 2.2652 | 1.97 | 83500 | 2.1100 | | 2.2655 | 1.98 | 84000 | 2.1101 | | 2.2683 | 1.99 | 84500 | 2.1100 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.2