generated_from_keras_callback

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Heem/distilroberta-finetuned-wtner

This model is a fine-tuned version of distilroberta-base 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

Train Loss Validation Loss Train Precision Train Recall Train F1 Train Accuracy Epoch
1.3579 0.8909 0.0 0.0 0.0 0.7744 0
0.7332 0.6231 0.3526 0.2926 0.3198 0.8256 1
0.5037 0.4471 0.3927 0.3755 0.3839 0.8575 2
0.3675 0.3776 0.484 0.5284 0.5052 0.8855 3
0.2890 0.3519 0.5149 0.6026 0.5553 0.9039 4
0.2367 0.3317 0.5820 0.6507 0.6144 0.9150 5
0.1942 0.2970 0.6220 0.6900 0.6542 0.9237 6
0.1599 0.3040 0.6375 0.6681 0.6525 0.9217 7
0.1281 0.3037 0.6774 0.7336 0.7044 0.9304 8
0.1097 0.3127 0.708 0.7729 0.7390 0.9309 9
0.0915 0.3114 0.6836 0.7642 0.7216 0.9290 10
0.0765 0.3190 0.7072 0.8122 0.7561 0.9372 11
0.0665 0.3169 0.7154 0.7904 0.7510 0.9353 12
0.0543 0.3251 0.7059 0.7860 0.7438 0.9329 13
0.0472 0.3307 0.7181 0.8122 0.7623 0.9357 14
0.0427 0.3639 0.7148 0.7991 0.7546 0.9357 15
0.0380 0.3373 0.7373 0.8210 0.7769 0.9377 16
0.0380 0.3422 0.7449 0.8035 0.7731 0.9372 17
0.0304 0.3455 0.7530 0.8122 0.7815 0.9386 18
0.0271 0.3584 0.7294 0.8122 0.7686 0.9377 19
0.0249 0.3661 0.7291 0.7991 0.7625 0.9377 20
0.0205 0.3683 0.7352 0.8122 0.7718 0.9391 21
0.0212 0.3855 0.7331 0.8035 0.7667 0.9382 22
0.0188 0.3814 0.7419 0.8035 0.7715 0.9391 23
0.0189 0.3889 0.7352 0.8122 0.7718 0.9357 24
0.0161 0.3913 0.7379 0.7991 0.7673 0.9382 25
0.0154 0.3872 0.7470 0.8122 0.7782 0.9406 26
0.0144 0.3934 0.7326 0.8253 0.7762 0.9401 27
0.0154 0.4167 0.7255 0.8079 0.7645 0.9343 28
0.0135 0.3976 0.7341 0.8079 0.7692 0.9362 29
0.0119 0.4118 0.7510 0.8297 0.7884 0.9382 30
0.0103 0.4112 0.7323 0.8122 0.7702 0.9372 31
0.0103 0.4172 0.7362 0.8166 0.7743 0.9382 32
0.0111 0.4157 0.7283 0.8079 0.7660 0.9382 33
0.0103 0.4152 0.7262 0.7991 0.7609 0.9372 34
0.0117 0.4090 0.7188 0.8035 0.7588 0.9377 35
0.0098 0.4268 0.7302 0.8035 0.7651 0.9367 36
0.0082 0.4354 0.7233 0.7991 0.7593 0.9362 37
0.0096 0.4298 0.7154 0.7904 0.7510 0.9357 38
0.0093 0.4294 0.7273 0.8035 0.7635 0.9362 39
0.0084 0.4266 0.7298 0.7904 0.7589 0.9348 40
0.0076 0.4230 0.7251 0.7948 0.7583 0.9357 41
0.0068 0.4243 0.7075 0.7817 0.7427 0.9329 42
0.0080 0.4379 0.7137 0.7729 0.7421 0.9338 43
0.0067 0.4361 0.7302 0.8035 0.7651 0.9362 44
0.0066 0.4377 0.7341 0.8079 0.7692 0.9367 45
0.0056 0.4357 0.7222 0.7948 0.7568 0.9362 46
0.0060 0.4393 0.7205 0.7991 0.7578 0.9362 47
0.0060 0.4429 0.7194 0.7948 0.7552 0.9357 48
0.0054 0.4416 0.7312 0.8079 0.7676 0.9367 49
0.0060 0.4413 0.7188 0.8035 0.7588 0.9362 50
0.0058 0.4381 0.7344 0.8210 0.7753 0.9377 51
0.0063 0.4388 0.7309 0.7948 0.7615 0.9377 52
0.0057 0.4402 0.7412 0.8253 0.7810 0.9382 53
0.0052 0.4381 0.7362 0.8166 0.7743 0.9377 54
0.0049 0.4407 0.7362 0.8166 0.7743 0.9377 55
0.0050 0.4394 0.7490 0.8210 0.7833 0.9386 56
0.0047 0.4481 0.7460 0.8210 0.7817 0.9382 57
0.0052 0.4544 0.748 0.8166 0.7808 0.9367 58
0.0049 0.4501 0.7430 0.8079 0.7741 0.9362 59
0.0050 0.4504 0.744 0.8122 0.7766 0.9367 60
0.0047 0.4517 0.7312 0.8079 0.7676 0.9372 61
0.0049 0.4526 0.7450 0.8166 0.7792 0.9382 62
0.0049 0.4534 0.7490 0.8210 0.7833 0.9386 63
0.0056 0.4543 0.748 0.8166 0.7808 0.9386 64
0.0044 0.4522 0.7410 0.8122 0.775 0.9382 65
0.0047 0.4522 0.7410 0.8122 0.775 0.9382 66
0.0050 0.4521 0.7410 0.8122 0.775 0.9382 67
0.0049 0.4521 0.7410 0.8122 0.775 0.9382 68
0.0055 0.4521 0.7410 0.8122 0.775 0.9382 69

Framework versions