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HYU-NLP-Bert-MultiIntent

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
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HYU-NLP-Bert-MultiIntent / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: HYU-NLP-Bert-MultiIntent
    results: []

HYU-NLP-Bert-MultiIntent

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: 0.0088
  • F1: 0.9503
  • Roc Auc: 0.9706
  • Accuracy: 0.8817

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 60

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.071 1.0 1801 0.0704 0.0 0.5 0.0
0.0515 2.0 3602 0.0488 0.0654 0.5170 0.0152
0.0325 3.0 5403 0.0275 0.468 0.6546 0.0912
0.0159 4.0 7204 0.0161 0.8725 0.8969 0.6041
0.008 5.0 9005 0.0108 0.9368 0.9550 0.8235
0.0049 6.0 10806 0.0085 0.9412 0.9614 0.8493
0.0028 7.0 12607 0.0072 0.9450 0.9668 0.8612
0.0019 8.0 14408 0.0071 0.9440 0.9663 0.8638
0.0012 9.0 16209 0.0068 0.9479 0.9688 0.8711
0.001 10.0 18010 0.0072 0.9439 0.9655 0.8566
0.0006 11.0 19811 0.0073 0.948 0.9696 0.8718
0.0005 12.0 21612 0.0075 0.9459 0.9680 0.8678
0.0004 13.0 23413 0.0074 0.9462 0.9677 0.8698
0.0003 14.0 25214 0.0082 0.9456 0.9691 0.8724
0.0002 15.0 27015 0.0078 0.9461 0.9685 0.8652
0.0002 16.0 28816 0.0081 0.9473 0.9691 0.8764
0.0001 17.0 30617 0.0080 0.9484 0.9690 0.8751
0.0001 18.0 32418 0.0100 0.9392 0.9656 0.8559
0.0002 19.0 34219 0.0085 0.9453 0.9667 0.8638
0.0001 20.0 36020 0.0087 0.948 0.9696 0.8764
0.0001 21.0 37821 0.0086 0.9485 0.9701 0.8764
0.0001 22.0 39622 0.0088 0.9503 0.9706 0.8817
0.0001 23.0 41423 0.0093 0.9468 0.9686 0.8685
0.0001 24.0 43224 0.0098 0.9424 0.9650 0.8553
0.0001 25.0 45025 0.0100 0.9418 0.9676 0.8553
0.0001 26.0 46826 0.0098 0.9478 0.9690 0.8724
0.0001 27.0 48627 0.0094 0.9493 0.9718 0.8771
0.0001 28.0 50428 0.0091 0.9482 0.9704 0.8718

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.20.3