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mdeberta-v3-base_ordinal_5_seed69_multilingual

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deberta-v2
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metadata
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - f1
  - precision
  - recall
  - accuracy
model-index:
  - name: mdeberta-v3-base_ordinal_5_seed69_multilingual
    results: []

mdeberta-v3-base_ordinal_5_seed69_multilingual

This model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2865
  • Mse: 2.5355
  • Rmse: 1.5923
  • Mae: 1.0336
  • R2: 0.2365
  • F1: 0.7527
  • Precision: 0.7544
  • Recall: 0.7591
  • Accuracy: 0.7591

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: 5e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Mse Rmse Mae R2 F1 Precision Recall Accuracy
3.4461 0.1449 100 3.2319 3.3564 1.8321 1.7300 -0.0820 0.5372 0.4484 0.6697 0.6697
3.1709 0.2899 200 2.9852 3.1868 1.7852 1.5359 -0.0273 0.5372 0.4484 0.6697 0.6697
3.0364 0.4348 300 2.8881 3.2227 1.7952 1.5359 -0.0389 0.5372 0.4484 0.6697 0.6697
2.8969 0.5797 400 2.7693 3.0057 1.7337 1.3271 0.0311 0.5372 0.4484 0.6697 0.6697
2.8529 0.7246 500 2.6898 2.8148 1.6777 1.3434 0.0926 0.5372 0.4484 0.6697 0.6697
2.781 0.8696 600 2.6252 2.7806 1.6675 1.3010 0.1036 0.5372 0.4484 0.6697 0.6697
2.6867 1.0145 700 2.5549 2.8711 1.6944 1.2088 0.0744 0.5893 0.6724 0.6827 0.6827
2.4738 1.1594 800 2.5246 2.8687 1.6937 1.1574 0.0752 0.7006 0.6990 0.7129 0.7129
2.4723 1.3043 900 2.4213 2.6020 1.6131 1.1126 0.1612 0.6983 0.7216 0.7300 0.7300
2.5362 1.4493 1000 2.4109 2.5506 1.5971 1.1884 0.1778 0.7136 0.7133 0.7259 0.7259
2.4032 1.5942 1100 2.4424 2.7520 1.6589 1.0799 0.1128 0.7040 0.7158 0.7284 0.7284
2.3445 1.7391 1200 2.3788 2.6387 1.6244 1.0954 0.1494 0.7336 0.7315 0.7390 0.7390
2.364 1.8841 1300 2.3767 2.6533 1.6289 1.0595 0.1446 0.7150 0.7298 0.7390 0.7390
2.3271 2.0290 1400 2.4050 2.7684 1.6638 1.1060 0.1076 0.7180 0.7161 0.7268 0.7268
2.1281 2.1739 1500 2.4391 2.7920 1.6709 1.0775 0.0999 0.7238 0.7231 0.7341 0.7341
2.1314 2.3188 1600 2.3782 2.6688 1.6337 1.0799 0.1396 0.7338 0.7323 0.7357 0.7357
2.1279 2.4638 1700 2.3962 2.7325 1.6530 1.0538 0.1191 0.7227 0.7335 0.7431 0.7431
2.0826 2.6087 1800 2.3376 2.6142 1.6168 1.0449 0.1573 0.7335 0.7351 0.7455 0.7455
2.1644 2.7536 1900 2.3175 2.5522 1.5976 1.0791 0.1772 0.7312 0.7293 0.7341 0.7341
2.0112 2.8986 2000 2.2738 2.5220 1.5881 1.0261 0.1870 0.7459 0.7452 0.7537 0.7537
1.9844 3.0435 2100 2.3487 2.6860 1.6389 1.0122 0.1341 0.7405 0.7410 0.7504 0.7504
1.8639 3.1884 2200 2.3881 2.7716 1.6648 1.0082 0.1065 0.7447 0.7431 0.7471 0.7471
1.7698 3.3333 2300 2.4045 2.8018 1.6739 0.9927 0.0968 0.7484 0.7465 0.7520 0.7520

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.19.1