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BERT_B09

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

  • Loss: 0.2572
  • Precision: 0.6376
  • Recall: 0.6753
  • F1: 0.6559
  • Accuracy: 0.9287

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: 4e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
  • label_smoothing_factor: 0.001

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.4255 1.0 46 0.3653 0.4807 0.5043 0.4922 0.9019
0.2621 2.0 92 0.2719 0.6056 0.6101 0.6078 0.9227
0.1642 3.0 138 0.2659 0.6047 0.6605 0.6314 0.9246
0.1249 4.0 184 0.2580 0.6382 0.6617 0.6498 0.9299
0.1232 5.0 230 0.2572 0.6376 0.6753 0.6559 0.9287

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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