generated_from_trainer

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hubert-large-korean-finetuned-korspeech-ser

This model is a fine-tuned version of team-lucid/hubert-large-korean on the None 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 Macro F1 Accuracy Weighted f1 Micro f1 Weighted recall Micro recall Macro recall Weighted precision Micro precision Macro precision
1.3419 0.28 100 1.2665 0.4034 0.4276 0.4034 0.4276 0.4276 0.4276 0.4276 0.4520 0.4276 0.4520
1.2356 0.57 200 1.2013 0.4506 0.4702 0.4506 0.4702 0.4702 0.4702 0.4702 0.4645 0.4702 0.4645
1.1905 0.85 300 1.2031 0.4480 0.4957 0.4480 0.4957 0.4957 0.4957 0.4957 0.5256 0.4957 0.5256
1.1335 1.13 400 1.1278 0.5083 0.5178 0.5083 0.5178 0.5178 0.5178 0.5178 0.5178 0.5178 0.5178
1.0794 1.42 500 1.0613 0.5507 0.5518 0.5507 0.5518 0.5518 0.5518 0.5518 0.5511 0.5518 0.5511
1.0535 1.7 600 1.0259 0.5517 0.5618 0.5517 0.5618 0.5618 0.5618 0.5618 0.5630 0.5618 0.5630
1.0396 1.99 700 1.0276 0.5514 0.5554 0.5514 0.5554 0.5554 0.5554 0.5554 0.5557 0.5554 0.5557
0.9841 2.27 800 1.0614 0.5440 0.5611 0.5440 0.5611 0.5611 0.5611 0.5611 0.5698 0.5611 0.5698
0.9527 2.55 900 1.0643 0.5405 0.5547 0.5405 0.5547 0.5547 0.5547 0.5547 0.5646 0.5547 0.5646
0.9616 2.84 1000 0.9971 0.5850 0.5895 0.5850 0.5895 0.5895 0.5895 0.5895 0.5924 0.5895 0.5924
0.9284 3.12 1100 1.0220 0.5626 0.5717 0.5626 0.5717 0.5717 0.5717 0.5717 0.5808 0.5717 0.5808
0.8744 3.4 1200 1.0368 0.5778 0.5831 0.5778 0.5831 0.5831 0.5831 0.5831 0.5884 0.5831 0.5884
0.8654 3.69 1300 1.0320 0.5925 0.5980 0.5925 0.5980 0.5980 0.5980 0.5980 0.6057 0.5980 0.6057
0.8716 3.97 1400 0.9965 0.5875 0.5930 0.5875 0.5930 0.5930 0.5930 0.5930 0.6001 0.5930 0.6001
0.7969 4.26 1500 0.9810 0.6102 0.6122 0.6102 0.6122 0.6122 0.6122 0.6122 0.6176 0.6122 0.6176
0.799 4.54 1600 0.9982 0.6075 0.6072 0.6075 0.6072 0.6072 0.6072 0.6072 0.6094 0.6072 0.6094
0.7785 4.82 1700 0.9812 0.6092 0.6136 0.6092 0.6136 0.6136 0.6136 0.6136 0.6136 0.6136 0.6136
0.7653 5.11 1800 0.9913 0.6124 0.6165 0.6124 0.6165 0.6165 0.6165 0.6165 0.6208 0.6165 0.6208
0.7192 5.39 1900 1.0057 0.6201 0.6207 0.6201 0.6207 0.6207 0.6207 0.6207 0.6244 0.6207 0.6244
0.7201 5.67 2000 1.0022 0.6207 0.6229 0.6207 0.6229 0.6229 0.6229 0.6229 0.6249 0.6229 0.6249
0.7351 5.96 2100 1.0010 0.6297 0.6321 0.6297 0.6321 0.6321 0.6321 0.6321 0.6354 0.6321 0.6354

Framework versions