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GPT-2_para3M_2epoch_256

Text Generation
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gpt2
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text-generation-inference
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metadata
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 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