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DeBERTa v3 (small) fine-tuned on MRPC

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

  • Loss: 0.2787
  • Accuracy: 0.8922
  • F1: 0.9233
  • Combined Score: 0.9078

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-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Combined Score
No log 1.0 230 0.2787 0.8922 0.9233 0.9078
No log 2.0 460 0.3651 0.875 0.9137 0.8944
No log 3.0 690 0.5238 0.8799 0.9179 0.8989
No log 4.0 920 0.4712 0.8946 0.9222 0.9084
0.2147 5.0 1150 0.5704 0.8946 0.9262 0.9104
0.2147 6.0 1380 0.5697 0.8995 0.9284 0.9140
0.2147 7.0 1610 0.6651 0.8922 0.9214 0.9068
0.2147 8.0 1840 0.6726 0.8946 0.9239 0.9093
0.0183 9.0 2070 0.7250 0.8848 0.9177 0.9012
0.0183 10.0 2300 0.7093 0.8922 0.9223 0.9072

Framework versions

  • Transformers 4.13.0.dev0
  • Pytorch 1.10.0+cu111
  • Datasets 1.15.1
  • Tokenizers 0.10.3
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Dataset used to train mrm8488/deberta-v3-small-finetuned-mrpc

Evaluation results