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metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fine_tune_bert-base-cased
    results: []

fine_tune_bert-base-cased

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

  • Loss: 0.0842
  • Precision: 0.9376
  • Recall: 0.9541
  • F1: 0.9458
  • Accuracy: 0.9866

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2667 1.0 220 0.0739 0.8619 0.9118 0.8862 0.9776
0.0602 2.0 440 0.0641 0.9109 0.9357 0.9231 0.9830
0.0361 3.0 660 0.0594 0.9187 0.9401 0.9293 0.9845
0.0234 4.0 880 0.0564 0.9233 0.9461 0.9346 0.9854
0.0164 5.0 1100 0.0585 0.9211 0.9465 0.9336 0.9856
0.0123 6.0 1320 0.0656 0.9212 0.9483 0.9346 0.9850
0.0084 7.0 1540 0.0639 0.9290 0.9514 0.9401 0.9864
0.0072 8.0 1760 0.0735 0.9325 0.9482 0.9403 0.9862
0.0051 9.0 1980 0.0745 0.9319 0.9488 0.9403 0.9856
0.0042 10.0 2200 0.0783 0.9308 0.9490 0.9398 0.9858
0.0034 11.0 2420 0.0782 0.9337 0.9509 0.9422 0.9862
0.0026 12.0 2640 0.0822 0.9328 0.9505 0.9416 0.9858
0.0019 13.0 2860 0.0785 0.9335 0.9525 0.9429 0.9862
0.0018 14.0 3080 0.0819 0.9382 0.9525 0.9453 0.9865
0.0015 15.0 3300 0.0846 0.9349 0.9524 0.9436 0.9863
0.0013 16.0 3520 0.0880 0.9353 0.9519 0.9435 0.9860
0.0012 17.0 3740 0.0846 0.9362 0.9527 0.9444 0.9864
0.001 18.0 3960 0.0868 0.9374 0.9532 0.9453 0.9864
0.0009 19.0 4180 0.0842 0.9381 0.9536 0.9458 0.9868
0.0009 20.0 4400 0.0842 0.9376 0.9541 0.9458 0.9866

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1