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metadata
base_model: vinai/phobert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
model-index:
  - name: cls-comment-phobert-base-v2-v2.2
    results: []

cls-comment-phobert-base-v2-v2.2

This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3163
  • Accuracy: 0.9317
  • F1 Score: 0.8875
  • Recall: 0.8777
  • Precision: 0.9015

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: 1e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 4000

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Score Recall Precision
1.663 1.05 100 1.4870 0.5041 0.1117 0.1667 0.0840
1.294 2.11 200 0.9956 0.6975 0.3911 0.3900 0.4902
0.898 3.16 300 0.6779 0.8232 0.5499 0.5697 0.5354
0.6411 4.21 400 0.5164 0.8568 0.5740 0.5895 0.5613
0.5031 5.26 500 0.4106 0.8938 0.7181 0.7114 0.7319
0.38 6.32 600 0.3474 0.9096 0.8326 0.8145 0.8739
0.2927 7.37 700 0.3110 0.9142 0.8598 0.8455 0.8810
0.2532 8.42 800 0.3046 0.9188 0.8702 0.8551 0.8881
0.2049 9.47 900 0.2851 0.9218 0.8689 0.8539 0.8902
0.1785 10.53 1000 0.2802 0.9251 0.8769 0.8561 0.9045
0.1511 11.58 1100 0.2875 0.9231 0.8744 0.8748 0.8770
0.1392 12.63 1200 0.2811 0.9264 0.8775 0.8597 0.9005
0.1166 13.68 1300 0.2757 0.9248 0.8751 0.8746 0.8786
0.1087 14.74 1400 0.2727 0.9258 0.8804 0.8761 0.8858
0.0918 15.79 1500 0.2862 0.9284 0.8830 0.8712 0.8988
0.0824 16.84 1600 0.2915 0.9291 0.8833 0.8689 0.9009
0.0745 17.89 1700 0.2994 0.9291 0.8797 0.8796 0.8847
0.0743 18.95 1800 0.3092 0.9254 0.8783 0.8686 0.8910
0.0636 20.0 1900 0.3142 0.9291 0.8811 0.8743 0.8916
0.0605 21.05 2000 0.3099 0.9291 0.8823 0.8700 0.8974
0.0501 22.11 2100 0.3163 0.9317 0.8875 0.8777 0.9015
0.0519 23.16 2200 0.3290 0.9297 0.8837 0.8692 0.9011
0.0464 24.21 2300 0.3406 0.9274 0.8805 0.8772 0.8872
0.0432 25.26 2400 0.3305 0.9284 0.8810 0.8775 0.8876
0.0404 26.32 2500 0.3378 0.9294 0.8826 0.8785 0.8901
0.0416 27.37 2600 0.3436 0.9284 0.8830 0.8726 0.8977

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2