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nb-bert-large-user-needs-v2

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bert
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metadata
license: cc-by-4.0
base_model: NbAiLab/nb-bert-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: nb-bert-large-user-needs-v2
    results: []

nb-bert-large-user-needs-v2

This model is a fine-tuned version of NbAiLab/nb-bert-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0173
  • Accuracy: 0.8
  • F1: 0.7945
  • Precision: 0.7947
  • Recall: 0.8

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 188 0.7673 0.696 0.6619 0.6566 0.696
No log 2.0 376 0.5713 0.7707 0.7423 0.7163 0.7707
0.6847 3.0 564 0.5849 0.7653 0.7547 0.7654 0.7653
0.6847 4.0 752 0.7731 0.7467 0.7254 0.7474 0.7467
0.6847 5.0 940 0.6056 0.7733 0.7740 0.7756 0.7733
0.4443 6.0 1128 0.7752 0.792 0.7877 0.7901 0.792
0.4443 7.0 1316 1.0173 0.8 0.7945 0.7947 0.8
0.2807 8.0 1504 1.1683 0.7813 0.7789 0.7783 0.7813
0.2807 9.0 1692 1.1886 0.7893 0.7825 0.7841 0.7893
0.2807 10.0 1880 1.3052 0.776 0.7695 0.7729 0.776
0.1282 11.0 2068 1.4641 0.784 0.7769 0.7804 0.784
0.1282 12.0 2256 1.5614 0.7813 0.7716 0.7871 0.7813
0.1282 13.0 2444 1.6424 0.784 0.7774 0.7804 0.784
0.0529 14.0 2632 1.7250 0.7813 0.7767 0.7770 0.7813
0.0529 15.0 2820 1.6061 0.8 0.7934 0.8058 0.8
0.0182 16.0 3008 1.7678 0.792 0.7854 0.7908 0.792
0.0182 17.0 3196 1.8226 0.7893 0.7834 0.7849 0.7893
0.0182 18.0 3384 1.8330 0.7973 0.7906 0.7936 0.7973
0.0061 19.0 3572 1.8423 0.7947 0.7879 0.7909 0.7947
0.0061 20.0 3760 1.8536 0.7973 0.7906 0.7936 0.7973

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.1.0
  • Datasets 2.18.0
  • Tokenizers 0.15.2