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update model card README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: SloBertAA_Top100_WithOOC_082023_MultilingualBertBase
    results: []

SloBertAA_Top100_WithOOC_082023_MultilingualBertBase

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: 1.8608
  • Accuracy: 0.6898
  • F1: 0.6904
  • Precision: 0.6936
  • Recall: 0.6898

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: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
1.7313 1.0 45122 1.6826 0.5773 0.5766 0.5997 0.5773
1.4117 2.0 90244 1.4419 0.6341 0.6345 0.6529 0.6341
1.1573 3.0 135366 1.3509 0.6614 0.6620 0.6733 0.6614
0.9147 4.0 180488 1.3583 0.6695 0.6699 0.6817 0.6695
0.7452 5.0 225610 1.3881 0.6797 0.6800 0.6887 0.6797
0.5393 6.0 270732 1.4650 0.6828 0.6835 0.6897 0.6828
0.4207 7.0 315854 1.5770 0.6839 0.6840 0.6905 0.6839
0.2985 8.0 360976 1.6813 0.6869 0.6877 0.6921 0.6869
0.2029 9.0 406098 1.7977 0.6882 0.6886 0.6923 0.6882
0.1546 10.0 451220 1.8608 0.6898 0.6904 0.6936 0.6898

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.8.0
  • Datasets 2.10.1
  • Tokenizers 0.13.2