generated_from_trainer

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scenario-non-kd-from-post-finetune-div-3-data-smsa-model-haryoaw-scenario-normal

This model is a fine-tuned version of haryoaw/scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base on the smsa 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 Accuracy F1
No log 0.29 100 2.4831 0.8627 0.8218
No log 0.58 200 2.1439 0.8794 0.8395
No log 0.87 300 1.8993 0.8794 0.8349
No log 1.16 400 1.6748 0.9016 0.8653
2.1604 1.45 500 1.4642 0.9079 0.8721
2.1604 1.74 600 1.3711 0.9032 0.8713
2.1604 2.03 700 1.3105 0.9063 0.8698
2.1604 2.33 800 1.5244 0.9016 0.8628
2.1604 2.62 900 1.3230 0.9079 0.8664
1.0815 2.91 1000 1.3788 0.9056 0.8646
1.0815 3.2 1100 1.3904 0.9008 0.8564
1.0815 3.49 1200 1.2840 0.9016 0.8606
1.0815 3.78 1300 1.2202 0.9119 0.8672
1.0815 4.07 1400 1.2470 0.9095 0.8697
0.7704 4.36 1500 1.2117 0.9079 0.8739
0.7704 4.65 1600 1.2606 0.9071 0.8590
0.7704 4.94 1700 1.1274 0.9143 0.8775
0.7704 5.23 1800 1.2533 0.9079 0.8689
0.7704 5.52 1900 1.1480 0.9032 0.8618
0.6037 5.81 2000 1.2233 0.9079 0.8679
0.6037 6.1 2100 1.1481 0.9143 0.8701
0.6037 6.4 2200 1.0861 0.9103 0.8750
0.6037 6.69 2300 1.1242 0.9167 0.8744
0.6037 6.98 2400 1.2090 0.9135 0.8723
0.5526 7.27 2500 1.2028 0.9095 0.8634
0.5526 7.56 2600 1.1548 0.9095 0.8756
0.5526 7.85 2700 1.1701 0.9119 0.8752
0.5526 8.14 2800 1.0309 0.9183 0.8821
0.5526 8.43 2900 1.0086 0.9119 0.8758
0.4881 8.72 3000 1.0807 0.9119 0.8708
0.4881 9.01 3100 1.0132 0.9095 0.8660
0.4881 9.3 3200 1.0036 0.9151 0.8765
0.4881 9.59 3300 1.0357 0.9103 0.8767
0.4881 9.88 3400 1.0252 0.9183 0.8783
0.4584 10.17 3500 1.0297 0.9063 0.8659
0.4584 10.47 3600 0.9843 0.9151 0.8739
0.4584 10.76 3700 0.9939 0.9159 0.8844
0.4584 11.05 3800 0.9474 0.9183 0.8788
0.4584 11.34 3900 1.0958 0.9103 0.8739
0.4192 11.63 4000 1.0178 0.9119 0.8696
0.4192 11.92 4100 0.9041 0.9159 0.8820
0.4192 12.21 4200 1.0919 0.9159 0.8878
0.4192 12.5 4300 0.9391 0.9167 0.8762
0.4192 12.79 4400 0.9506 0.9175 0.8742
0.4068 13.08 4500 0.9086 0.9119 0.8754
0.4068 13.37 4600 0.9865 0.9135 0.8704
0.4068 13.66 4700 0.9853 0.9183 0.8810
0.4068 13.95 4800 0.9321 0.9151 0.8755
0.4068 14.24 4900 0.8807 0.9167 0.8846
0.368 14.53 5000 0.9752 0.9190 0.8885
0.368 14.83 5100 0.8790 0.9198 0.8799
0.368 15.12 5200 0.9361 0.9143 0.8756
0.368 15.41 5300 0.9677 0.9143 0.8777
0.368 15.7 5400 0.8967 0.9119 0.8742
0.3543 15.99 5500 0.9003 0.9175 0.8824
0.3543 16.28 5600 0.8932 0.9143 0.8724
0.3543 16.57 5700 1.0152 0.9159 0.8770
0.3543 16.86 5800 1.0595 0.9119 0.8718
0.3543 17.15 5900 0.9360 0.9151 0.8755
0.3381 17.44 6000 0.9166 0.9143 0.8791
0.3381 17.73 6100 0.9094 0.9135 0.8773
0.3381 18.02 6200 0.9357 0.9135 0.8779
0.3381 18.31 6300 0.8833 0.9206 0.8863
0.3381 18.6 6400 0.9473 0.9119 0.8696
0.3151 18.9 6500 0.9133 0.9151 0.8756

Framework versions