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This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5875
  • Accuracy: 0.4339

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.06 100 1.8239 0.3638
No log 0.13 200 1.7266 0.3971
No log 0.19 300 1.6873 0.4040
No log 0.25 400 1.6609 0.4188
1.8118 0.32 500 1.6674 0.4048
1.8118 0.38 600 1.6381 0.4172
1.8118 0.45 700 1.6437 0.4156
1.8118 0.51 800 1.6378 0.4143
1.8118 0.57 900 1.6301 0.4214
1.6738 0.64 1000 1.6106 0.4320
1.6738 0.7 1100 1.6089 0.4259
1.6738 0.76 1200 1.5988 0.4299
1.6738 0.83 1300 1.5951 0.4347
1.6738 0.89 1400 1.5896 0.4320
1.6488 0.96 1500 1.5875 0.4339

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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