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abdiharyadi/deberta-v3-large-ft-icar-a-v0.11-2e-6

🤗 Hugging Face sourcetext-classificationmit435M params1.7 GBsafetensors✓ 5 checksumsupdated today
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deberta-v3-large-ft-icar-a-v0.11

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: 0.8122
  • Accuracy: 0.9204
  • Precision: 0.9146
  • Recall: 0.9067
  • F1: 0.9100

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: 2e-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
2.6872 1.0 871 0.6573 0.8453 0.8798 0.7874 0.7908
1.9067 2.0 1742 0.7585 0.8806 0.9047 0.8402 0.8553
1.4171 3.0 2613 0.6031 0.9081 0.8979 0.9058 0.9008
1.1236 4.0 3484 0.7067 0.9127 0.9108 0.9003 0.9044
1.0755 5.0 4355 0.8168 0.8974 0.9084 0.8688 0.8839
0.8824 6.0 5226 0.7756 0.9112 0.9031 0.9139 0.9063
0.651 7.0 6097 0.7307 0.9158 0.9165 0.9019 0.9080
0.6468 8.0 6968 0.8609 0.9112 0.9006 0.8949 0.8966
0.4654 9.0 7839 0.8359 0.9096 0.8910 0.9053 0.8974
0.4483 10.0 8710 0.7813 0.9158 0.9107 0.8999 0.9044
0.3246 11.0 9581 0.8845 0.9158 0.9025 0.9096 0.9051
0.2939 12.0 10452 0.8122 0.9204 0.9146 0.9067 0.9100
0.2661 13.0 11323 1.0101 0.9096 0.8986 0.8994 0.8983
0.2874 14.0 12194 0.9044 0.9127 0.9043 0.9057 0.9038
0.2304 15.0 13065 1.0021 0.9066 0.8940 0.9120 0.8988
0.1991 16.0 13936 0.8841 0.9158 0.9052 0.9034 0.9036
0.1957 17.0 14807 1.0139 0.9096 0.9035 0.9025 0.9013
0.116 18.0 15678 0.9821 0.9127 0.9049 0.9042 0.9027

Framework versions

  • Transformers 4.52.4
  • Pytorch 2.6.0+cu124
  • Datasets 4.0.0
  • Tokenizers 0.21.2