generated_from_trainer

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scenario-kd-from-post-finetune-gold-silver-div-2-8000-data-smsa-model-haryoaw-sc

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 0.4 100 1.5313 0.8960 0.8525
No log 0.8 200 1.5166 0.8984 0.8531
No log 1.2 300 1.4584 0.8968 0.8390
No log 1.6 400 1.1168 0.9175 0.8830
1.3988 2.0 500 1.5231 0.8952 0.8554
1.3988 2.4 600 1.1988 0.9111 0.8775
1.3988 2.8 700 1.1218 0.9103 0.8743
1.3988 3.2 800 1.0986 0.9143 0.8766
1.3988 3.6 900 1.1321 0.9167 0.8737
0.6891 4.0 1000 1.0806 0.9103 0.8752
0.6891 4.4 1100 0.9855 0.9190 0.8878
0.6891 4.8 1200 1.1468 0.9127 0.8784
0.6891 5.2 1300 1.1624 0.9071 0.8699
0.6891 5.6 1400 0.8455 0.9183 0.8836
0.5436 6.0 1500 0.9643 0.9183 0.8825
0.5436 6.4 1600 0.9413 0.9087 0.8734
0.5436 6.8 1700 0.9458 0.9190 0.8890
0.5436 7.2 1800 0.9271 0.9127 0.8769
0.5436 7.6 1900 0.9143 0.9111 0.8754
0.4723 8.0 2000 0.9558 0.9151 0.8741
0.4723 8.4 2100 1.0649 0.9198 0.8807
0.4723 8.8 2200 0.8562 0.9183 0.8829
0.4723 9.2 2300 0.8350 0.9159 0.8828
0.4723 9.6 2400 0.8073 0.9159 0.8823
0.4263 10.0 2500 0.9557 0.9159 0.8791
0.4263 10.4 2600 0.9259 0.9159 0.8838
0.4263 10.8 2700 0.8317 0.9230 0.8917
0.4263 11.2 2800 0.8210 0.9183 0.8833
0.4263 11.6 2900 0.9217 0.9230 0.8881
0.3875 12.0 3000 0.7284 0.9183 0.8858
0.3875 12.4 3100 0.9787 0.9190 0.8848
0.3875 12.8 3200 0.8598 0.9159 0.8732
0.3875 13.2 3300 0.8222 0.9175 0.8805
0.3875 13.6 3400 0.8813 0.9222 0.8919
0.351 14.0 3500 0.9317 0.9230 0.8848
0.351 14.4 3600 0.9120 0.9159 0.8823
0.351 14.8 3700 0.7626 0.9270 0.8922
0.351 15.2 3800 0.8003 0.9206 0.8829
0.351 15.6 3900 0.8165 0.9238 0.8922
0.3285 16.0 4000 0.8977 0.9214 0.8863
0.3285 16.4 4100 0.8437 0.9151 0.8724
0.3285 16.8 4200 0.8861 0.9190 0.8884
0.3285 17.2 4300 0.7916 0.9254 0.8953
0.3285 17.6 4400 0.8829 0.9230 0.8915
0.3151 18.0 4500 0.8952 0.9183 0.8866
0.3151 18.4 4600 0.8962 0.9103 0.8746
0.3151 18.8 4700 0.8192 0.9190 0.8831
0.3151 19.2 4800 0.8661 0.9151 0.8860
0.3151 19.6 4900 0.8064 0.9198 0.8853
0.2938 20.0 5000 0.7614 0.9214 0.8891
0.2938 20.4 5100 0.8073 0.9190 0.8840
0.2938 20.8 5200 0.9247 0.9063 0.8653
0.2938 21.2 5300 0.7755 0.9159 0.8778
0.2938 21.6 5400 0.7942 0.9190 0.8786
0.2859 22.0 5500 0.7626 0.9175 0.8839
0.2859 22.4 5600 0.8849 0.9190 0.8804
0.2859 22.8 5700 0.7944 0.9183 0.8829
0.2859 23.2 5800 0.8265 0.9198 0.8871

Framework versions