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phobert-base-v2-DACN1

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phobert-base-v2-DACN1 / README.md
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metadata
base_model: vinai/phobert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: phobert-base-v2-DACN1
    results: []

phobert-base-v2-DACN1

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: 0.5520
  • Accuracy: 0.8784
  • F1: 0.8781

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.2782 200 0.4102 0.8061 0.8037
No log 0.5563 400 0.3549 0.8456 0.8461
No log 0.8345 600 0.3583 0.8466 0.8454
0.4175 1.1127 800 0.3401 0.8537 0.8523
0.4175 1.3908 1000 0.3179 0.8639 0.8646
0.4175 1.6690 1200 0.3148 0.8687 0.8691
0.4175 1.9471 1400 0.3240 0.8574 0.8582
0.3061 2.2253 1600 0.3148 0.8734 0.8740
0.3061 2.5035 1800 0.3224 0.8742 0.8743
0.3061 2.7816 2000 0.3288 0.8678 0.8671
0.2524 3.0598 2200 0.3512 0.8767 0.8769
0.2524 3.3380 2400 0.3421 0.8798 0.8796
0.2524 3.6161 2600 0.3089 0.8795 0.8799
0.2524 3.8943 2800 0.3569 0.8718 0.8725
0.2123 4.1725 3000 0.3840 0.8747 0.8744
0.2123 4.4506 3200 0.3681 0.8729 0.8736
0.2123 4.7288 3400 0.3575 0.8725 0.8732
0.1771 5.0070 3600 0.3575 0.8793 0.8794
0.1771 5.2851 3800 0.4285 0.8758 0.8752
0.1771 5.5633 4000 0.3843 0.8778 0.8782
0.1771 5.8414 4200 0.3951 0.8780 0.8779
0.1479 6.1196 4400 0.4364 0.8734 0.8726
0.1479 6.3978 4600 0.4273 0.8753 0.8752
0.1479 6.6759 4800 0.4596 0.8786 0.8783
0.1479 6.9541 5000 0.4498 0.8784 0.8785
0.1284 7.2323 5200 0.4592 0.8793 0.8795
0.1284 7.5104 5400 0.4796 0.8755 0.8747
0.1284 7.7886 5600 0.4830 0.8729 0.8722
0.1068 8.0668 5800 0.4879 0.8789 0.8787
0.1068 8.3449 6000 0.5213 0.8767 0.8761
0.1068 8.6231 6200 0.5114 0.8769 0.8764
0.1068 8.9013 6400 0.5090 0.8778 0.8777
0.0946 9.1794 6600 0.5192 0.8800 0.8800
0.0946 9.4576 6800 0.5517 0.8753 0.8748
0.0946 9.7357 7000 0.5520 0.8784 0.8781

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1