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LIM-0.5

Token Classification
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bert
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LIM-0.5 / README.md
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metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: 20240402140914_big_aristotle
    results: []

20240402140914_big_aristotle

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

  • Loss: 0.0293
  • Precision: 0.9731
  • Recall: 0.9711
  • F1: 0.9721
  • Accuracy: 0.9891

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: 0.0005
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 69
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 350
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0654 0.09 300 0.0495 0.9535 0.9462 0.9498 0.9808
0.0647 0.18 600 0.0489 0.9529 0.9470 0.9499 0.9807
0.0626 0.26 900 0.0486 0.9542 0.9448 0.9495 0.9807
0.0613 0.35 1200 0.0460 0.9544 0.9501 0.9522 0.9815
0.0623 0.44 1500 0.0469 0.9538 0.9493 0.9515 0.9814
0.0573 0.53 1800 0.0455 0.9552 0.9488 0.9520 0.9816
0.0569 0.61 2100 0.0447 0.9528 0.9540 0.9534 0.9819
0.0585 0.7 2400 0.0464 0.9566 0.9468 0.9516 0.9815
0.056 0.79 2700 0.0452 0.9555 0.9517 0.9536 0.9822
0.0564 0.88 3000 0.0430 0.9591 0.9512 0.9551 0.9828
0.0552 0.96 3300 0.0433 0.9548 0.9532 0.9540 0.9825
0.048 1.05 3600 0.0444 0.9579 0.9529 0.9554 0.9828
0.0483 1.14 3900 0.0415 0.9582 0.9553 0.9568 0.9831
0.0465 1.23 4200 0.0424 0.9622 0.9495 0.9558 0.9831
0.0465 1.31 4500 0.0415 0.9616 0.9514 0.9565 0.9835
0.0462 1.4 4800 0.0407 0.9588 0.9534 0.9561 0.9835
0.0467 1.49 5100 0.0403 0.9582 0.9581 0.9581 0.9836
0.0453 1.58 5400 0.0405 0.9636 0.9513 0.9574 0.9839
0.0446 1.66 5700 0.0383 0.9637 0.9555 0.9596 0.9847
0.0443 1.75 6000 0.0382 0.9596 0.9572 0.9584 0.9844
0.0432 1.84 6300 0.0373 0.9637 0.9573 0.9605 0.9847
0.0424 1.93 6600 0.0368 0.9674 0.9516 0.9594 0.9850
0.0364 2.01 6900 0.0361 0.9633 0.9570 0.9601 0.9851
0.0358 2.1 7200 0.0366 0.9618 0.9613 0.9615 0.9853
0.0359 2.19 7500 0.0370 0.9665 0.9561 0.9613 0.9852
0.036 2.28 7800 0.0360 0.9660 0.9564 0.9611 0.9853
0.0352 2.36 8100 0.0355 0.9658 0.9602 0.9630 0.9856
0.0354 2.45 8400 0.0354 0.9682 0.9579 0.9630 0.9860
0.0347 2.54 8700 0.0347 0.9694 0.9566 0.9630 0.9861
0.0347 2.63 9000 0.0340 0.9676 0.9597 0.9636 0.9864
0.0338 2.71 9300 0.0327 0.9682 0.9626 0.9654 0.9867
0.0338 2.8 9600 0.0334 0.9681 0.9627 0.9654 0.9865
0.033 2.89 9900 0.0325 0.9705 0.9613 0.9659 0.9870
0.0326 2.98 10200 0.0331 0.9686 0.9640 0.9663 0.9870
0.0264 3.06 10500 0.0352 0.9689 0.9651 0.9670 0.9871
0.0261 3.15 10800 0.0329 0.9698 0.9633 0.9666 0.9871
0.026 3.24 11100 0.0328 0.9672 0.9662 0.9667 0.9872
0.0261 3.33 11400 0.0333 0.9678 0.9681 0.9680 0.9872
0.0264 3.41 11700 0.0326 0.9689 0.9676 0.9682 0.9875
0.0258 3.5 12000 0.0313 0.9717 0.9643 0.9680 0.9877
0.0254 3.59 12300 0.0307 0.9691 0.9675 0.9683 0.9880
0.0249 3.68 12600 0.0304 0.9720 0.9666 0.9693 0.9881
0.025 3.76 12900 0.0300 0.9686 0.9680 0.9683 0.9882
0.0242 3.85 13200 0.0297 0.9682 0.9682 0.9682 0.9881
0.0246 3.94 13500 0.0291 0.9725 0.9655 0.9690 0.9883
0.0184 4.03 13800 0.0320 0.9712 0.9678 0.9695 0.9882
0.0186 4.11 14100 0.0311 0.9703 0.9688 0.9696 0.9883
0.0183 4.2 14400 0.0319 0.9718 0.9696 0.9707 0.9886
0.0181 4.29 14700 0.0312 0.9730 0.9673 0.9702 0.9885
0.0181 4.38 15000 0.0308 0.9694 0.9698 0.9696 0.9885
0.0178 4.47 15300 0.0302 0.9727 0.9698 0.9712 0.9888
0.0175 4.55 15600 0.0300 0.9729 0.9705 0.9717 0.9889
0.0171 4.64 15900 0.0300 0.9725 0.9713 0.9719 0.9890
0.017 4.73 16200 0.0296 0.9712 0.9710 0.9711 0.9888
0.017 4.82 16500 0.0295 0.9726 0.9707 0.9717 0.9890
0.0168 4.9 16800 0.0297 0.9730 0.9711 0.9721 0.9891
0.0166 4.99 17100 0.0293 0.9731 0.9711 0.9721 0.9891

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.0a0+6a974be
  • Datasets 2.18.0
  • Tokenizers 0.15.2