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VP_ViSoBERT_syl_ViWikiFC

Text Classification
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Safetensors
xlm-roberta
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metadata
base_model: tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: VP_ViSoBERT_syl_ViWikiFC
    results: []

VP_ViSoBERT_syl_ViWikiFC

This model is a fine-tuned version of tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1555
  • Accuracy: 0.6445

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6994 0.05 100 0.9688 0.6158
0.6904 0.1 200 0.9753 0.6014
0.7969 0.14 300 0.9446 0.5871
0.6801 0.19 400 0.9912 0.6057
0.7089 0.24 500 0.9617 0.5861
0.6627 0.29 600 1.0585 0.5689
0.6792 0.33 700 1.0064 0.6230
0.6702 0.38 800 1.0593 0.5818
0.6252 0.43 900 0.9621 0.5967
0.6262 0.48 1000 1.0152 0.5957
0.6515 0.53 1100 0.9539 0.6225
0.6596 0.57 1200 0.9188 0.6067
0.6458 0.62 1300 0.9318 0.6201
0.6087 0.67 1400 0.9532 0.6172
0.6282 0.72 1500 1.0107 0.6244
0.6266 0.76 1600 1.0199 0.6096
0.6165 0.81 1700 1.0973 0.6096
0.5869 0.86 1800 0.9177 0.6325
0.596 0.91 1900 0.8821 0.6364
0.6073 0.96 2000 0.9350 0.6306
0.5921 1.0 2100 0.9606 0.6282
0.4551 1.05 2200 1.0386 0.6373
0.3922 1.1 2300 1.1936 0.6368
0.39 1.15 2400 1.1922 0.6316
0.442 1.19 2500 1.1599 0.6220
0.4092 1.24 2600 1.3106 0.6196
0.4582 1.29 2700 1.1817 0.6316
0.4356 1.34 2800 1.1257 0.6316
0.4145 1.39 2900 1.1899 0.6354
0.4379 1.43 3000 1.1385 0.6388
0.4222 1.48 3100 1.1844 0.6249
0.3758 1.53 3200 1.2444 0.6311
0.4114 1.58 3300 1.1908 0.6349
0.4449 1.62 3400 1.1483 0.6273
0.4046 1.67 3500 1.1977 0.6306
0.4274 1.72 3600 1.1520 0.6450
0.3785 1.77 3700 1.1665 0.6330
0.3854 1.82 3800 1.1680 0.6474
0.3562 1.86 3900 1.1616 0.6459
0.3938 1.91 4000 1.1823 0.6397
0.5083 1.96 4100 1.1555 0.6445

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2