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xlnet-base-cased

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metadata
license: mit
base_model: xlnet/xlnet-base-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: xlnet-base-cased
    results: []

xlnet-base-cased

This model is a fine-tuned version of xlnet/xlnet-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9234
  • Accuracy: 0.8218
  • Precision: 0.8189
  • Recall: 0.8218
  • Precision Macro: 0.7836
  • Recall Macro: 0.7606
  • Macro Fpr: 0.0159
  • Weighted Fpr: 0.0152
  • Weighted Specificity: 0.9756
  • Macro Specificity: 0.9865
  • Weighted Sensitivity: 0.8218
  • Macro Sensitivity: 0.7606
  • F1 Micro: 0.8218
  • F1 Macro: 0.7664
  • F1 Weighted: 0.8189

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:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall Precision Macro Recall Macro Macro Fpr Weighted Fpr Weighted Specificity Macro Specificity Weighted Sensitivity Macro Sensitivity F1 Micro F1 Macro F1 Weighted
1.2613 1.0 643 0.7758 0.7676 0.7673 0.7676 0.5269 0.5129 0.0220 0.0212 0.9680 0.9824 0.7676 0.5129 0.7676 0.4819 0.7524
0.7364 2.0 1286 0.6755 0.8071 0.8088 0.8071 0.7425 0.6972 0.0174 0.0168 0.9751 0.9855 0.8071 0.6972 0.8071 0.7019 0.8013
0.6021 3.0 1929 0.8443 0.8064 0.8016 0.8064 0.7270 0.7262 0.0176 0.0169 0.9718 0.9852 0.8064 0.7262 0.8064 0.7229 0.8014
0.4361 4.0 2572 0.8850 0.8002 0.8001 0.8002 0.7167 0.7048 0.0180 0.0175 0.9731 0.9849 0.8002 0.7048 0.8002 0.7051 0.7971
0.3359 5.0 3215 1.1264 0.8017 0.7981 0.8017 0.6531 0.6681 0.0181 0.0174 0.9732 0.9850 0.8017 0.6681 0.8017 0.6459 0.7962
0.2827 6.0 3858 1.1471 0.7994 0.8092 0.7994 0.7389 0.6922 0.0183 0.0176 0.9686 0.9845 0.7994 0.6922 0.7994 0.7042 0.7952
0.1945 7.0 4501 1.1841 0.8149 0.8129 0.8149 0.7850 0.7598 0.0166 0.0160 0.9746 0.9860 0.8149 0.7598 0.8149 0.7667 0.8122
0.1286 8.0 5144 1.3231 0.8079 0.8105 0.8079 0.7630 0.7216 0.0171 0.0167 0.9757 0.9856 0.8079 0.7216 0.8079 0.7283 0.8067
0.1304 9.0 5787 1.3869 0.8102 0.8118 0.8102 0.7705 0.7603 0.0171 0.0165 0.9741 0.9856 0.8102 0.7603 0.8102 0.7570 0.8088
0.0875 10.0 6430 1.6901 0.7823 0.7932 0.7823 0.7601 0.7020 0.0199 0.0195 0.9680 0.9834 0.7823 0.7020 0.7823 0.7192 0.7817
0.1075 11.0 7073 1.6517 0.7978 0.8021 0.7978 0.7513 0.7567 0.0183 0.0178 0.9758 0.9849 0.7978 0.7567 0.7978 0.7470 0.7935
0.0632 12.0 7716 1.5290 0.8149 0.8184 0.8149 0.7746 0.7772 0.0167 0.0160 0.9738 0.9859 0.8149 0.7772 0.8149 0.7707 0.8150
0.0565 13.0 8359 1.5766 0.8064 0.8107 0.8064 0.7528 0.7628 0.0174 0.0169 0.9769 0.9856 0.8064 0.7628 0.8064 0.7537 0.8061
0.0504 14.0 9002 1.7548 0.8048 0.8100 0.8048 0.7569 0.7702 0.0174 0.0170 0.9765 0.9854 0.8048 0.7702 0.8048 0.7553 0.8046
0.0295 15.0 9645 1.7570 0.8102 0.8226 0.8102 0.7705 0.7611 0.0168 0.0165 0.9770 0.9858 0.8102 0.7611 0.8102 0.7610 0.8141
0.0338 16.0 10288 1.7394 0.8110 0.8138 0.8110 0.7639 0.7659 0.0168 0.0164 0.9775 0.9859 0.8110 0.7659 0.8110 0.7613 0.8100
0.0444 17.0 10931 1.7975 0.8118 0.8201 0.8118 0.7511 0.7610 0.0168 0.0163 0.9775 0.9859 0.8118 0.7610 0.8118 0.7457 0.8129
0.0397 18.0 11574 1.6921 0.8149 0.8203 0.8149 0.7540 0.7854 0.0165 0.0160 0.9780 0.9862 0.8149 0.7854 0.8149 0.7553 0.8130
0.0356 19.0 12217 1.6908 0.8273 0.8307 0.8273 0.7764 0.7992 0.0152 0.0147 0.9784 0.9870 0.8273 0.7992 0.8273 0.7814 0.8265
0.0306 20.0 12860 1.8374 0.8180 0.8208 0.8180 0.7635 0.7756 0.0162 0.0156 0.9771 0.9863 0.8180 0.7756 0.8180 0.7620 0.8166
0.0234 21.0 13503 1.7738 0.8195 0.8185 0.8195 0.7947 0.7602 0.0160 0.0155 0.9760 0.9864 0.8195 0.7602 0.8195 0.7713 0.8174
0.0091 22.0 14146 1.8537 0.8172 0.8167 0.8172 0.7732 0.7646 0.0163 0.0157 0.9764 0.9862 0.8172 0.7646 0.8172 0.7654 0.8143
0.0138 23.0 14789 1.8306 0.8102 0.8173 0.8102 0.7729 0.7569 0.0167 0.0165 0.9757 0.9857 0.8102 0.7569 0.8102 0.7625 0.8125
0.0213 24.0 15432 1.9363 0.8125 0.8149 0.8125 0.7777 0.7540 0.0168 0.0162 0.9739 0.9858 0.8125 0.7540 0.8125 0.7622 0.8115
0.0034 25.0 16075 1.9552 0.8156 0.8179 0.8156 0.7843 0.7583 0.0165 0.0159 0.9740 0.9860 0.8156 0.7583 0.8156 0.7657 0.8147
0.0028 26.0 16718 1.9404 0.8172 0.8163 0.8172 0.7884 0.7591 0.0164 0.0157 0.9747 0.9861 0.8172 0.7591 0.8172 0.7656 0.8137
0.0105 27.0 17361 1.9156 0.8180 0.8132 0.8180 0.7848 0.7575 0.0164 0.0156 0.9742 0.9861 0.8180 0.7575 0.8180 0.7667 0.8140
0.0048 28.0 18004 1.9104 0.8203 0.8196 0.8203 0.7877 0.7615 0.0160 0.0154 0.9758 0.9864 0.8203 0.7615 0.8203 0.7658 0.8175
0.0011 29.0 18647 1.9312 0.8203 0.8185 0.8203 0.7888 0.7600 0.0161 0.0154 0.9755 0.9864 0.8203 0.7600 0.8203 0.7664 0.8173
0.0004 30.0 19290 1.9234 0.8218 0.8189 0.8218 0.7836 0.7606 0.0159 0.0152 0.9756 0.9865 0.8218 0.7606 0.8218 0.7664 0.8189

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.15.1