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deberta-v3-base_finetuned_nostalgia

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deberta-v2
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metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: deberta-v3-base_finetuned_nostalgia
    results: []

deberta-v3-base_finetuned_nostalgia

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3772
  • Accuracy: 0.9379
  • F1 Macro: 0.9288
  • Accuracy Balanced: 0.9264
  • F1 Micro: 0.9379
  • Precision Macro: 0.9313
  • Recall Macro: 0.9264
  • Precision Micro: 0.9379
  • Recall Micro: 0.9379

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-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 1984
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro Accuracy Balanced F1 Micro Precision Macro Recall Macro Precision Micro Recall Micro
No log 1.0 73 0.3903 0.8759 0.8470 0.8251 0.8759 0.8890 0.8251 0.8759 0.8759
No log 2.0 146 0.2130 0.9103 0.8933 0.8783 0.9103 0.9149 0.8783 0.9103 0.9103
No log 3.0 219 0.1253 0.9379 0.9288 0.9264 0.9379 0.9313 0.9264 0.9379 0.9379
No log 4.0 292 0.2694 0.9310 0.9229 0.9324 0.9310 0.9154 0.9324 0.9310 0.9310
No log 5.0 365 0.1924 0.9448 0.9370 0.9370 0.9448 0.9370 0.9370 0.9448 0.9448
No log 6.0 438 0.2648 0.9379 0.9288 0.9264 0.9379 0.9313 0.9264 0.9379 0.9379
0.1908 7.0 511 0.3431 0.9379 0.9288 0.9264 0.9379 0.9313 0.9264 0.9379 0.9379
0.1908 8.0 584 0.3450 0.9379 0.9288 0.9264 0.9379 0.9313 0.9264 0.9379 0.9379
0.1908 9.0 657 0.3538 0.9379 0.9279 0.9209 0.9379 0.9362 0.9209 0.9379 0.9379
0.1908 10.0 730 0.3772 0.9379 0.9288 0.9264 0.9379 0.9313 0.9264 0.9379 0.9379

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1