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chatgpt-mlm

This model is a fine-tuned version of on the tmp/cnn_clean_chatgpt_data/ dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4969
  • Accuracy: 0.7015

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 6
  • num_epochs: 75.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
7.2061 0.2 500 6.6958 0.1161
6.6051 0.4 1000 6.5527 0.1303
6.5016 0.6 1500 6.4720 0.1382
6.4189 0.8 2000 6.3796 0.1424
6.3648 1.0 2500 6.3224 0.1448
6.2787 1.2 3000 6.2787 0.1411
6.2583 1.4 3500 6.2467 0.1446
6.2211 1.6 4000 6.2162 0.1475
6.1897 1.8 4500 6.1933 0.1466
6.1625 2.0 5000 6.1704 0.1483
6.1412 2.2 5500 6.1527 0.1484
6.1062 2.4 6000 6.1296 0.1492
6.1003 2.6 6500 6.1275 0.1483
6.0944 2.8 7000 6.0983 0.1496
6.077 3.0 7500 6.0839 0.1509
6.0419 3.2 8000 6.0747 0.1504
6.0264 3.4 8500 6.0729 0.1506
6.0222 3.6 9000 6.0585 0.1504
6.0067 3.8 9500 6.0518 0.1500
6.0045 4.0 10000 6.0300 0.1504
5.9659 4.2 10500 6.0248 0.1504
5.9542 4.4 11000 6.0143 0.1512
5.9479 4.6 11500 5.9891 0.1514
5.9506 4.8 12000 5.9827 0.1517
5.9358 5.0 12500 5.9973 0.1509
5.9114 5.2 13000 5.9761 0.1505
5.9089 5.4 13500 5.9637 0.1516
5.9008 5.6 14000 5.9535 0.1515
5.9007 5.8 14500 5.9343 0.1530
5.8734 6.0 15000 5.9255 0.1532
5.8519 6.2 15500 5.9213 0.1527
5.8383 6.4 16000 5.9126 0.1513
5.8461 6.6 16500 5.9041 0.1525
5.8387 6.8 17000 5.8923 0.1517
5.831 7.0 17500 5.8782 0.1558
5.8003 7.2 18000 5.8660 0.1554
5.7832 7.4 18500 5.8508 0.1560
5.7902 7.6 19000 5.8495 0.1558
5.7707 7.8 19500 5.8376 0.1553
5.7638 8.0 20000 5.8289 0.1564
5.741 8.2 20500 5.8230 0.1574
5.7291 8.4 21000 5.8110 0.1574
5.7206 8.6 21500 5.8014 0.1575
5.6974 8.8 22000 5.7644 0.1605
5.6954 9.0 22500 5.7404 0.1638
5.6467 9.2 23000 5.7040 0.1668
5.6134 9.4 23500 5.6656 0.1738
5.5855 9.6 24000 5.6262 0.1787
5.5374 9.8 24500 5.5587 0.1883
5.4678 10.0 25000 5.4388 0.2009
5.3324 10.2 25500 5.2703 0.2203
5.1849 10.4 26000 5.0908 0.2434
5.0273 10.6 26500 4.9103 0.2657
4.8718 10.8 27000 4.7637 0.2844
4.7523 11.0 27500 4.6064 0.3023
4.5814 11.2 28000 4.4398 0.3220
4.4627 11.4 28500 4.3005 0.3376
4.3228 11.6 29000 4.1771 0.3520
4.1885 11.8 29500 4.0783 0.3632
4.0772 12.0 30000 3.9658 0.3765
3.9602 12.2 30500 3.8686 0.3880
3.8622 12.4 31000 3.7886 0.3968
3.7958 12.6 31500 3.6968 0.4074
3.7245 12.8 32000 3.6480 0.4129
3.6503 13.0 32500 3.5771 0.4204
3.5569 13.2 33000 3.5103 0.4286
3.5151 13.4 33500 3.4611 0.4358
3.4388 13.6 34000 3.4119 0.4410
3.41 13.8 34500 3.3570 0.4486
3.3447 14.0 35000 3.3158 0.4518
3.2678 14.2 35500 3.2717 0.4585
3.2395 14.4 36000 3.2234 0.4629
3.2033 14.6 36500 3.1723 0.4697
3.1739 14.8 37000 3.1409 0.4747
3.1467 15.0 37500 3.1042 0.4782
3.0736 15.2 38000 3.0561 0.4839
3.0468 15.4 38500 3.0275 0.4869
3.0105 15.6 39000 3.0051 0.4898
2.9828 15.8 39500 2.9689 0.4950
2.9523 16.0 40000 2.9481 0.4959
2.8951 16.2 40500 2.8918 0.5039
2.8614 16.4 41000 2.8734 0.5054
2.8422 16.6 41500 2.8487 0.5083
2.8184 16.8 42000 2.8223 0.5138
2.7806 17.0 42500 2.7965 0.5167
2.7356 17.2 43000 2.7596 0.5209
2.7357 17.4 43500 2.7407 0.5250
2.7015 17.6 44000 2.7135 0.5272
2.688 17.8 44500 2.6935 0.5289
2.6582 18.0 45000 2.6572 0.5342
2.6186 18.2 45500 2.6396 0.5357
2.6071 18.4 46000 2.6270 0.5377
2.5891 18.6 46500 2.6110 0.5407
2.558 18.8 47000 2.5874 0.5435
2.5521 19.0 47500 2.5540 0.5465
2.5086 19.2 48000 2.5296 0.5504
2.4933 19.4 48500 2.5199 0.5523
2.4924 19.6 49000 2.5037 0.5550
2.4633 19.8 49500 2.4792 0.5567
2.4426 20.0 50000 2.4724 0.5600
2.4106 20.2 50500 2.4396 0.5626
2.4103 20.4 51000 2.4259 0.5631
2.3783 20.6 51500 2.4072 0.5672
2.3712 20.8 52000 2.4055 0.5679
2.3616 21.0 52500 2.3781 0.5724
2.3274 21.2 53000 2.3627 0.5746
2.3133 21.4 53500 2.3586 0.5751
2.3076 21.6 54000 2.3207 0.5785
2.2991 21.8 54500 2.3152 0.5796
2.2831 22.0 55000 2.3001 0.5815
2.2461 22.2 55500 2.2944 0.5822
2.2467 22.4 56000 2.2849 0.5856
2.2199 22.6 56500 2.2776 0.5863
2.2279 22.8 57000 2.2577 0.5885
2.2048 23.0 57500 2.2566 0.5886
2.1704 23.2 58000 2.2453 0.5914
2.1682 23.4 58500 2.2314 0.5927
2.1592 23.6 59000 2.2097 0.5961
2.1547 23.8 59500 2.1984 0.5972
2.1558 24.0 60000 2.1866 0.5993
2.1189 24.2 60500 2.1675 0.6009
2.1088 24.4 61000 2.1613 0.6028
2.1164 24.6 61500 2.1531 0.6046
2.094 24.8 62000 2.1507 0.6041
2.0977 25.0 62500 2.1299 0.6063
2.0657 25.2 63000 2.1218 0.6071
2.051 25.4 63500 2.1233 0.6083
2.0482 25.6 64000 2.1069 0.6100
2.04 25.8 64500 2.0985 0.6120
2.0341 26.0 65000 2.0929 0.6128
2.0207 26.2 65500 2.0767 0.6151
2.0044 26.4 66000 2.0672 0.6162
2.0037 26.6 66500 2.0623 0.6159
2.0081 26.8 67000 2.0614 0.6164
1.9847 27.0 67500 2.0499 0.6186
1.9465 27.2 68000 2.0399 0.6200
1.9573 27.4 68500 2.0353 0.6210
1.9682 27.6 69000 2.0187 0.6227
1.9573 27.8 69500 2.0251 0.6229
1.9491 28.0 70000 2.0086 0.6245
1.903 28.2 70500 2.0067 0.6246
1.9152 28.4 71000 1.9929 0.6264
1.9188 28.6 71500 1.9857 0.6274
1.9232 28.8 72000 1.9796 0.6287
1.9011 29.0 72500 1.9791 0.6289
1.8733 29.2 73000 1.9700 0.6289
1.8731 29.4 73500 1.9584 0.6303
1.8812 29.6 74000 1.9573 0.6323
1.8674 29.8 74500 1.9501 0.6318
1.8572 30.0 75000 1.9454 0.6333
1.849 30.2 75500 1.9375 0.6352
1.8332 30.4 76000 1.9344 0.6343
1.8413 30.6 76500 1.9293 0.6340
1.8298 30.8 77000 1.9228 0.6371
1.8336 31.0 77500 1.9215 0.6372
1.8122 31.2 78000 1.9133 0.6387
1.8001 31.4 78500 1.9119 0.6383
1.7934 31.6 79000 1.9088 0.6387
1.8079 31.8 79500 1.8940 0.6417
1.8017 32.0 80000 1.8889 0.6410
1.7789 32.2 80500 1.8883 0.6423
1.7739 32.4 81000 1.8836 0.6419
1.7602 32.6 81500 1.8795 0.6433
1.7731 32.8 82000 1.8769 0.6439
1.7784 33.0 82500 1.8590 0.6467
1.7506 33.2 83000 1.8664 0.6447
1.7307 33.4 83500 1.8553 0.6472
1.748 33.6 84000 1.8523 0.6470
1.7285 33.8 84500 1.8397 0.6491
1.7426 34.0 85000 1.8321 0.6492
1.7128 34.2 85500 1.8220 0.6507
1.7155 34.4 86000 1.8487 0.6479
1.7143 34.6 86500 1.8267 0.6504
1.7197 34.8 87000 1.8368 0.6499
1.7043 35.0 87500 1.8128 0.6524
1.6931 35.2 88000 1.8212 0.6517
1.6873 35.4 88500 1.8110 0.6531
1.684 35.6 89000 1.8145 0.6529
1.6802 35.8 89500 1.8046 0.6537
1.6807 36.0 90000 1.8016 0.6550
1.6612 36.2 90500 1.7997 0.6539
1.6586 36.4 91000 1.8014 0.6537
1.658 36.6 91500 1.7938 0.6565
1.6623 36.8 92000 1.7776 0.6586
1.6618 37.0 92500 1.7884 0.6573
1.6453 37.2 93000 1.7871 0.6571
1.6462 37.4 93500 1.7781 0.6585
1.6353 37.6 94000 1.7808 0.6583
1.6507 37.8 94500 1.7666 0.6603
1.6383 38.0 95000 1.7624 0.6606
1.6299 38.2 95500 1.7653 0.6605
1.6085 38.4 96000 1.7523 0.6610
1.6155 38.6 96500 1.7521 0.6612
1.6106 38.8 97000 1.7634 0.6605
1.6201 39.0 97500 1.7461 0.6625
1.5835 39.2 98000 1.7505 0.6617
1.5885 39.4 98500 1.7477 0.6623
1.5988 39.6 99000 1.7445 0.6635
1.6013 39.8 99500 1.7407 0.6637
1.594 40.0 100000 1.7336 0.6656
1.5741 40.2 100500 1.7348 0.6637
1.5744 40.4 101000 1.7242 0.6653
1.5809 40.6 101500 1.7262 0.6661
1.5723 40.8 102000 1.7257 0.6665
1.5695 41.0 102500 1.7182 0.6664
1.5462 41.2 103000 1.7257 0.6660
1.5545 41.4 103500 1.7101 0.6686
1.5574 41.6 104000 1.7108 0.6684
1.5485 41.8 104500 1.7164 0.6665
1.5487 42.0 105000 1.7080 0.6694
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1.5468 42.6 106500 1.7058 0.6704
1.5375 42.8 107000 1.7020 0.6689
1.5301 43.0 107500 1.6950 0.6710
1.5224 43.2 108000 1.6990 0.6702
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1.5191 44.0 110000 1.6877 0.6715
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1.4496 47.2 118000 1.6490 0.6772
1.457 47.4 118500 1.6462 0.6778
1.4541 47.6 119000 1.6368 0.6799
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1.4406 48.2 120500 1.6374 0.6799
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1.4395 48.6 121500 1.6425 0.6787
1.4347 48.8 122000 1.6252 0.6814
1.4392 49.0 122500 1.6360 0.6801
1.439 49.2 123000 1.6233 0.6826
1.4223 49.4 123500 1.6262 0.6809
1.4292 49.6 124000 1.6292 0.6811
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1.4241 50.0 125000 1.6230 0.6810
1.4118 50.2 125500 1.6256 0.6822
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1.3876 51.8 129500 1.6078 0.6841
1.4027 52.0 130000 1.6028 0.6847
1.3939 52.2 130500 1.6081 0.6845
1.391 52.4 131000 1.6034 0.6849
1.3895 52.6 131500 1.6016 0.6850
1.3858 52.8 132000 1.6010 0.6847
1.3852 53.0 132500 1.5886 0.6862
1.3716 53.2 133000 1.5964 0.6862
1.3727 53.4 133500 1.5952 0.6875
1.3656 53.6 134000 1.6031 0.6850
1.3873 53.8 134500 1.5927 0.6867
1.3742 54.0 135000 1.5970 0.6858
1.3687 54.2 135500 1.5954 0.6863
1.359 54.4 136000 1.5854 0.6873
1.3696 54.6 136500 1.5902 0.6878
1.38 54.8 137000 1.5870 0.6871
1.3529 55.0 137500 1.5888 0.6879
1.3479 55.2 138000 1.5720 0.6889
1.3558 55.4 138500 1.5810 0.6877
1.3565 55.6 139000 1.5687 0.6909
1.351 55.8 139500 1.5762 0.6897
1.3698 56.0 140000 1.5785 0.6881
1.3388 56.2 140500 1.5767 0.6882
1.3433 56.4 141000 1.5752 0.6896
1.3505 56.6 141500 1.5754 0.6890
1.3429 56.8 142000 1.5772 0.6896
1.337 57.0 142500 1.5732 0.6900
1.3398 57.2 143000 1.5681 0.6904
1.3334 57.4 143500 1.5696 0.6900
1.3384 57.6 144000 1.5674 0.6908
1.33 57.8 144500 1.5592 0.6916
1.327 58.0 145000 1.5498 0.6924
1.3234 58.2 145500 1.5626 0.6910
1.3266 58.4 146000 1.5743 0.6893
1.3152 58.6 146500 1.5680 0.6912
1.3279 58.8 147000 1.5581 0.6919
1.3172 59.0 147500 1.5645 0.6917
1.3073 59.2 148000 1.5579 0.6924
1.307 59.4 148500 1.5468 0.6939
1.3164 59.6 149000 1.5519 0.6930
1.3037 59.8 149500 1.5628 0.6917
1.3171 60.0 150000 1.5489 0.6934
1.3035 60.2 150500 1.5499 0.6931
1.3109 60.4 151000 1.5608 0.6922
1.304 60.6 151500 1.5612 0.6915
1.3104 60.8 152000 1.5511 0.6933
1.3071 61.0 152500 1.5469 0.6935
1.2935 61.2 153000 1.5485 0.6942
1.2866 61.4 153500 1.5463 0.6940
1.2926 61.6 154000 1.5406 0.6956
1.3029 61.8 154500 1.5424 0.6945
1.2921 62.0 155000 1.5446 0.6944
1.2765 62.2 155500 1.5397 0.6953
1.275 62.4 156000 1.5469 0.6945
1.2909 62.6 156500 1.5427 0.6945
1.2869 62.8 157000 1.5388 0.6949
1.2883 63.0 157500 1.5375 0.6948
1.2673 63.2 158000 1.5423 0.6948
1.2754 63.4 158500 1.5360 0.6957
1.2772 63.6 159000 1.5331 0.6952
1.283 63.8 159500 1.5354 0.6955
1.2737 64.0 160000 1.5388 0.6961
1.2681 64.2 160500 1.5382 0.6952
1.2769 64.4 161000 1.5350 0.6958
1.2668 64.6 161500 1.5345 0.6956
1.2795 64.8 162000 1.5198 0.6984
1.2632 65.0 162500 1.5324 0.6965
1.2646 65.2 163000 1.5420 0.6963
1.2739 65.4 163500 1.5308 0.6972
1.2667 65.6 164000 1.5220 0.6978
1.26 65.8 164500 1.5283 0.6961
1.2714 66.0 165000 1.5235 0.6977
1.2652 66.2 165500 1.5270 0.6973
1.2554 66.4 166000 1.5356 0.6959
1.2666 66.6 166500 1.5231 0.6968
1.2634 66.8 167000 1.5170 0.6994
1.2485 67.0 167500 1.5205 0.6988
1.2397 67.2 168000 1.5263 0.6982
1.2416 67.4 168500 1.5133 0.7001
1.2615 67.6 169000 1.5150 0.6990
1.254 67.8 169500 1.5213 0.6990
1.2463 68.0 170000 1.5157 0.6995
1.2412 68.2 170500 1.5082 0.7000
1.248 68.4 171000 1.5168 0.6992
1.2468 68.6 171500 1.5187 0.6991
1.246 68.8 172000 1.5094 0.7001
1.2443 69.0 172500 1.5186 0.6984
1.2451 69.2 173000 1.5157 0.6988
1.2375 69.4 173500 1.5102 0.7002
1.2441 69.6 174000 1.5143 0.7000
1.2335 69.8 174500 1.5173 0.6985
1.2361 70.0 175000 1.5102 0.7001
1.23 70.2 175500 1.5155 0.6997
1.2401 70.4 176000 1.5027 0.7005
1.2346 70.6 176500 1.5123 0.6995
1.2306 70.8 177000 1.5151 0.6984
1.2333 71.0 177500 1.5125 0.7000
1.2248 71.2 178000 1.5199 0.6992
1.2385 71.4 178500 1.5108 0.7000
1.2278 71.6 179000 1.5092 0.7000
1.2278 71.8 179500 1.5163 0.6989
1.2242 72.0 180000 1.5056 0.7010
1.2208 72.2 180500 1.4968 0.7023
1.2216 72.4 181000 1.5097 0.7006
1.2271 72.6 181500 1.4988 0.7013
1.2302 72.8 182000 1.5141 0.6997
1.2268 73.0 182500 1.4996 0.7016
1.2258 73.2 183000 1.5016 0.7008
1.2244 73.4 183500 1.5032 0.7012
1.2117 73.6 184000 1.5097 0.7003
1.2279 73.8 184500 1.5058 0.7012
1.2274 74.0 185000 1.5030 0.7015
1.2117 74.2 185500 1.5086 0.7008
1.2223 74.4 186000 1.4998 0.7018
1.227 74.6 186500 1.5059 0.7014
1.2168 74.8 187000 1.4984 0.7011
1.2093 75.0 187500 1.5077 0.7018

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.2
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Evaluation results

Model card error

This model's model-index metadata is invalid: Schema validation error. "model-index[0].results[0].dataset.type" with value "tmp/cnn_clean_chatgpt_data/" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/