generated_from_trainer

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retrain_epoch2and3

This model is a fine-tuned version of alexziweiwang/retrain_first1epoch 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 Acc Wer Correct Total Strlen
No log 0.02 5 7.8479 0.24 1.0 48 200 200
7.6019 0.04 10 7.4765 0.24 1.0 48 200 200
7.6019 0.06 15 7.1196 0.24 1.0 48 200 200
7.3222 0.08 20 6.8029 0.24 1.0 48 200 200
7.3222 0.11 25 6.5210 0.24 1.0 48 200 200
6.2645 0.13 30 6.2630 0.24 1.0 48 200 200
6.2645 0.15 35 6.0213 0.24 1.0 48 200 200
5.8699 0.17 40 5.8096 0.24 1.0 48 200 200
5.8699 0.19 45 5.5831 0.24 1.0 48 200 200
5.7145 0.21 50 5.3644 0.24 1.0 48 200 200
5.7145 0.23 55 5.1777 0.24 1.0 48 200 200
5.3702 0.25 60 5.0257 0.24 1.0 48 200 200
5.3702 0.27 65 4.8642 0.24 1.0 48 200 200
5.1896 0.3 70 4.7205 0.24 1.0 48 200 200
5.1896 0.32 75 4.5846 0.24 1.0 48 200 200
5.0615 0.34 80 4.4313 0.24 1.0 48 200 200
5.0615 0.36 85 4.2923 0.24 1.0 48 200 200
4.5189 0.38 90 4.1662 0.24 1.0 48 200 200
4.5189 0.4 95 4.0545 0.24 1.0 48 200 200
4.4911 0.42 100 3.9585 0.24 1.0 48 200 200
4.4911 0.44 105 3.8489 0.24 1.0 48 200 200
4.1997 0.46 110 3.7573 0.24 1.0 48 200 200
4.1997 0.48 115 3.6722 0.24 1.0 48 200 200
3.7348 0.51 120 3.5844 0.24 1.0 48 200 200
3.7348 0.53 125 3.4980 0.24 1.0 48 200 200
3.8042 0.55 130 3.4318 0.24 1.0 48 200 200
3.8042 0.57 135 3.3690 0.24 1.0 48 200 200
3.705 0.59 140 3.3126 0.24 1.0 48 200 200
3.705 0.61 145 3.2630 0.24 1.0 48 200 200
3.763 0.63 150 3.2063 0.24 1.0 48 200 200
3.763 0.65 155 3.1562 0.24 1.0 48 200 200
3.5585 0.67 160 3.1096 0.24 1.0 48 200 200
3.5585 0.7 165 3.0719 0.24 1.0 48 200 200
3.213 0.72 170 3.0373 0.24 1.0 48 200 200
3.213 0.74 175 3.0035 0.24 1.0 48 200 200
3.2874 0.76 180 2.9712 0.24 1.0 48 200 200
3.2874 0.78 185 2.9405 0.24 1.0 48 200 200
3.3327 0.8 190 2.9134 0.24 1.0 48 200 200
3.3327 0.82 195 2.8910 0.24 1.0 48 200 200
3.2382 0.84 200 2.8672 0.24 1.0 48 200 200
3.2382 0.86 205 2.8462 0.24 1.0 48 200 200
3.0069 0.89 210 2.8260 0.24 1.0 48 200 200
3.0069 0.91 215 2.8087 0.24 1.0 48 200 200
3.2288 0.93 220 2.7920 0.24 1.0 48 200 200
3.2288 0.95 225 2.7750 0.24 1.0 48 200 200
2.787 0.97 230 2.7557 0.24 1.0 48 200 200
2.787 0.99 235 2.7367 0.24 1.0 48 200 200
2.9717 1.01 240 2.7207 0.24 1.0 48 200 200
2.9717 1.03 245 2.7063 0.24 1.0 48 200 200
2.9269 1.05 250 2.6939 0.24 1.0 48 200 200
2.9269 1.08 255 2.6831 0.24 1.0 48 200 200
2.8771 1.1 260 2.6709 0.24 1.0 48 200 200
2.8771 1.12 265 2.6594 0.24 1.0 48 200 200
3.0474 1.14 270 2.6472 0.24 1.0 48 200 200
3.0474 1.16 275 2.6361 0.24 1.0 48 200 200
2.7652 1.18 280 2.6268 0.24 1.0 48 200 200
2.7652 1.2 285 2.6184 0.24 1.0 48 200 200
2.8322 1.22 290 2.6106 0.24 1.0 48 200 200
2.8322 1.24 295 2.6034 0.24 1.0 48 200 200
2.6464 1.27 300 2.5957 0.24 1.0 48 200 200
2.6464 1.29 305 2.5877 0.24 1.0 48 200 200
2.7974 1.31 310 2.5805 0.24 1.0 48 200 200
2.7974 1.33 315 2.5748 0.24 1.0 48 200 200
2.797 1.35 320 2.5698 0.24 1.0 48 200 200
2.797 1.37 325 2.5644 0.24 1.0 48 200 200
2.7508 1.39 330 2.5595 0.24 1.0 48 200 200
2.7508 1.41 335 2.5537 0.24 1.0 48 200 200
2.7188 1.43 340 2.5486 0.24 1.0 48 200 200
2.7188 1.46 345 2.5434 0.24 1.0 48 200 200
2.6889 1.48 350 2.5377 0.24 1.0 48 200 200
2.6889 1.5 355 2.5336 0.24 1.0 48 200 200
2.6373 1.52 360 2.5300 0.24 1.0 48 200 200
2.6373 1.54 365 2.5258 0.24 1.0 48 200 200
2.765 1.56 370 2.5219 0.24 1.0 48 200 200
2.765 1.58 375 2.5181 0.24 1.0 48 200 200
2.6407 1.6 380 2.5144 0.24 1.0 48 200 200
2.6407 1.62 385 2.5113 0.24 1.0 48 200 200
2.7727 1.64 390 2.5093 0.24 1.0 48 200 200
2.7727 1.67 395 2.5076 0.24 1.0 48 200 200
2.8091 1.69 400 2.5060 0.24 1.0 48 200 200
2.8091 1.71 405 2.5042 0.24 1.0 48 200 200
2.7204 1.73 410 2.5027 0.24 1.0 48 200 200
2.7204 1.75 415 2.5011 0.24 1.0 48 200 200
2.6168 1.77 420 2.4987 0.24 1.0 48 200 200
2.6168 1.79 425 2.4965 0.24 1.0 48 200 200
2.6947 1.81 430 2.4947 0.24 1.0 48 200 200
2.6947 1.83 435 2.4932 0.24 1.0 48 200 200
2.7495 1.86 440 2.4921 0.24 1.0 48 200 200
2.7495 1.88 445 2.4911 0.24 1.0 48 200 200
2.7413 1.9 450 2.4904 0.24 1.0 48 200 200
2.7413 1.92 455 2.4897 0.24 1.0 48 200 200
2.6498 1.94 460 2.4893 0.24 1.0 48 200 200
2.6498 1.96 465 2.4890 0.24 1.0 48 200 200
2.6891 1.98 470 2.4888 0.24 1.0 48 200 200

Framework versions