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bert
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: training
    results: []

training

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

  • Loss: 2.3256
  • Accuracy: 0.6768
  • F1: 0.6764
  • Precision: 0.6772
  • Recall: 0.6768

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: 40
  • eval_batch_size: 20
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 66 0.7029 0.4939 0.3623 0.4289 0.4939
No log 2.0 132 0.6985 0.4726 0.4074 0.4429 0.4726
No log 3.0 198 0.7052 0.5091 0.5079 0.5101 0.5091
No log 4.0 264 0.7277 0.5732 0.5687 0.5746 0.5732
No log 5.0 330 0.8226 0.5747 0.5711 0.5791 0.5747
No log 6.0 396 0.9070 0.6098 0.6084 0.6126 0.6098
No log 7.0 462 0.9877 0.6296 0.6288 0.6299 0.6296
0.4904 8.0 528 1.2868 0.5976 0.5814 0.6198 0.5976
0.4904 9.0 594 1.2709 0.6433 0.6396 0.6517 0.6433
0.4904 10.0 660 1.3541 0.6494 0.6494 0.6494 0.6494
0.4904 11.0 726 1.4138 0.6631 0.6572 0.6724 0.6631
0.4904 12.0 792 1.5116 0.6631 0.6616 0.6676 0.6631
0.4904 13.0 858 1.5349 0.6738 0.6687 0.6825 0.6738
0.4904 14.0 924 1.5437 0.6845 0.6845 0.6845 0.6845
0.4904 15.0 990 1.8465 0.6585 0.6581 0.6588 0.6585
0.0493 16.0 1056 1.8186 0.6662 0.6661 0.6667 0.6662
0.0493 17.0 1122 1.9234 0.6601 0.6589 0.6635 0.6601
0.0493 18.0 1188 1.9517 0.6707 0.6689 0.6763 0.6707
0.0493 19.0 1254 1.9673 0.6616 0.6609 0.6639 0.6616
0.0493 20.0 1320 2.0034 0.6768 0.6768 0.6769 0.6768
0.0493 21.0 1386 2.0452 0.6707 0.6707 0.6707 0.6707
0.0493 22.0 1452 2.1151 0.6570 0.6569 0.6578 0.6570
0.0085 23.0 1518 2.0888 0.6631 0.6627 0.6633 0.6631
0.0085 24.0 1584 2.1101 0.6646 0.6646 0.6649 0.6646
0.0085 25.0 1650 2.1330 0.6662 0.6661 0.6666 0.6662
0.0085 26.0 1716 2.1890 0.6662 0.6659 0.6663 0.6662
0.0085 27.0 1782 2.2275 0.6601 0.6598 0.6602 0.6601
0.0085 28.0 1848 2.2380 0.6662 0.6648 0.6704 0.6662
0.0085 29.0 1914 2.2606 0.6646 0.6646 0.6650 0.6646
0.0085 30.0 1980 2.2708 0.6738 0.6734 0.6755 0.6738
0.0029 31.0 2046 2.2827 0.6677 0.6675 0.6677 0.6677
0.0029 32.0 2112 2.2992 0.6738 0.6738 0.6738 0.6738
0.0029 33.0 2178 2.2926 0.6768 0.6757 0.6782 0.6768
0.0029 34.0 2244 2.3100 0.6738 0.6738 0.6740 0.6738
0.0029 35.0 2310 2.3081 0.6768 0.6767 0.6768 0.6768
0.0029 36.0 2376 2.3080 0.6768 0.6764 0.6772 0.6768
0.0029 37.0 2442 2.3242 0.6784 0.6783 0.6787 0.6784
0.0004 38.0 2508 2.3252 0.6799 0.6799 0.6799 0.6799
0.0004 39.0 2574 2.3228 0.6784 0.6782 0.6784 0.6784
0.0004 40.0 2640 2.3256 0.6768 0.6764 0.6772 0.6768

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.0
  • Tokenizers 0.15.0