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multibert1110_lrate2.5b16

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5408
  • Precisions: 0.8751
  • Recall: 0.8102
  • F-measure: 0.8365
  • Accuracy: 0.9131

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: 2.5e-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: 14

Training results

Training Loss Epoch Step Validation Loss Precisions Recall F-measure Accuracy
0.6226 1.0 236 0.3957 0.8372 0.6871 0.6960 0.8714
0.3373 2.0 472 0.3830 0.8460 0.7204 0.7485 0.8810
0.2071 3.0 708 0.3464 0.8572 0.7790 0.7966 0.8985
0.1384 4.0 944 0.4046 0.8653 0.7863 0.8128 0.9041
0.0935 5.0 1180 0.4299 0.8559 0.7976 0.8209 0.9044
0.0708 6.0 1416 0.4899 0.8709 0.7972 0.8269 0.9096
0.0504 7.0 1652 0.4837 0.8578 0.8030 0.8254 0.9039
0.0361 8.0 1888 0.5098 0.8448 0.7970 0.8173 0.9056
0.0259 9.0 2124 0.5260 0.8622 0.7992 0.8241 0.9090
0.0214 10.0 2360 0.5394 0.8676 0.8051 0.8316 0.9107
0.0149 11.0 2596 0.5408 0.8751 0.8102 0.8365 0.9131
0.0095 12.0 2832 0.5725 0.8709 0.8056 0.8321 0.9115
0.0092 13.0 3068 0.5650 0.8658 0.8099 0.8326 0.9119
0.0073 14.0 3304 0.5734 0.8637 0.8101 0.8317 0.9122

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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