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abdiharyadi/deberta-v3-large-ft-icar-a-v0.11-to

🤗 Hugging Face sourcetext-classificationmit435M params1.7 GBsafetensors✓ 6 checksumsupdated today
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deberta-v3-large-ft-icar-a-v0.11-to

This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8877
  • Accuracy: 0.9149
  • Precision: 0.8989
  • Recall: 0.8870
  • F1: 0.8925

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: 3e-06
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 3
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
2.2451 1.0 704 0.7087 0.8450 0.8182 0.7916 0.8022
1.7218 2.0 1408 0.7015 0.8771 0.8588 0.8321 0.8435
1.339 3.0 2112 0.8289 0.8790 0.8768 0.8236 0.8425
1.0953 4.0 2816 0.8203 0.8922 0.8892 0.8474 0.8619
0.8391 5.0 3520 0.7416 0.9055 0.8888 0.8803 0.8842
0.7217 6.0 4224 0.7420 0.9074 0.8879 0.8796 0.8835
0.6325 7.0 4928 0.7878 0.8960 0.9077 0.8313 0.8557
0.4921 8.0 5632 0.8229 0.8979 0.8749 0.8653 0.8698
0.4206 9.0 6336 0.9300 0.8866 0.8583 0.8624 0.8603
0.3445 10.0 7040 0.8305 0.9055 0.8881 0.8733 0.8799
0.2187 11.0 7744 1.0823 0.8809 0.8565 0.8357 0.8447
0.2072 12.0 8448 0.8940 0.9055 0.8939 0.8599 0.8723
0.1579 13.0 9152 0.9451 0.8979 0.8706 0.8640 0.8670
0.209 14.0 9856 0.8635 0.9036 0.8934 0.8643 0.8767
0.1432 15.0 10560 0.9687 0.9017 0.8992 0.8497 0.8678
0.0898 16.0 11264 0.8877 0.9149 0.8989 0.8870 0.8925
0.0516 17.0 11968 1.0159 0.9055 0.8930 0.8594 0.8723
0.0682 18.0 12672 1.1908 0.8866 0.8519 0.8718 0.8586
0.182 19.0 13376 0.9440 0.9112 0.8895 0.8935 0.8914
0.0817 20.0 14080 1.0778 0.8979 0.8846 0.8653 0.8739
0.1148 21.0 14784 1.0861 0.9055 0.8860 0.8746 0.8798
0.1034 22.0 15488 1.1888 0.8847 0.8585 0.8422 0.8485
0.108 23.0 16192 0.9938 0.9074 0.8900 0.8894 0.8895
0.032 24.0 16896 1.2295 0.8960 0.8803 0.8585 0.8677
0.0975 25.0 17600 1.1770 0.8998 0.8912 0.8679 0.8782
0.0226 26.0 18304 1.2022 0.8979 0.8869 0.8666 0.8757
0.0527 27.0 19008 1.0817 0.9093 0.9004 0.8760 0.8868
0.0174 28.0 19712 1.0276 0.9130 0.8942 0.8911 0.8925
0.061 29.0 20416 1.0123 0.9149 0.9030 0.8814 0.8904
0.0496 30.0 21120 1.0771 0.9093 0.8990 0.8716 0.8829
0.0436 31.0 21824 1.1075 0.9036 0.8809 0.8885 0.8845

Framework versions

  • Transformers 4.52.4
  • Pytorch 2.6.0+cu124
  • Datasets 4.4.1
  • Tokenizers 0.21.2