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gg-bert-base-uncased

Text Classification
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xlm-roberta
Generated from Trainer
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gg-bert-base-uncased / README.md
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metadata
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: gg-bert-base-uncased
    results: []

gg-bert-base-uncased

This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7791
  • Accuracy: 0.752
  • Precision: 0.7388
  • Recall: 0.7570
  • F1: 0.7396

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: 0.0002
  • 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
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
2.2541 1.0 469 2.2063 0.1488 0.2698 0.1552 0.1036
1.8967 2.0 938 1.8773 0.5168 0.5264 0.5331 0.4788
1.5747 3.0 1407 1.5546 0.5984 0.6125 0.6118 0.5636
1.4206 4.0 1876 1.3029 0.6528 0.6732 0.6666 0.6224
1.2804 5.0 2345 1.1876 0.6928 0.6972 0.6989 0.6844
1.1587 6.0 2814 1.0644 0.7136 0.7105 0.7136 0.6858
1.1589 7.0 3283 0.9883 0.7216 0.7173 0.7261 0.7031
1.0745 8.0 3752 0.9485 0.728 0.7151 0.7318 0.7195
1.0348 9.0 4221 0.9278 0.7328 0.7306 0.7372 0.7166
1.0019 10.0 4690 0.9114 0.72 0.7316 0.7231 0.7006
1.0204 11.0 5159 0.8967 0.7152 0.7187 0.7215 0.6895
1.0651 12.0 5628 0.8574 0.7424 0.7446 0.7474 0.7327
0.9841 13.0 6097 0.8461 0.7328 0.7495 0.7370 0.7076
0.9794 14.0 6566 0.8510 0.7248 0.7157 0.7319 0.7022
1.0242 15.0 7035 0.8127 0.7264 0.7112 0.7300 0.6998
0.9614 16.0 7504 0.8146 0.7312 0.7210 0.7376 0.7149
0.9358 17.0 7973 0.8288 0.736 0.7487 0.7439 0.7275
0.9719 18.0 8442 0.7958 0.7488 0.7403 0.7530 0.7349
0.9159 19.0 8911 0.7973 0.7472 0.7388 0.7522 0.7357
0.9824 20.0 9380 0.7921 0.7504 0.7439 0.7562 0.7363
1.0215 21.0 9849 0.7831 0.7536 0.7415 0.7586 0.7392
0.9191 22.0 10318 0.7780 0.7504 0.7387 0.7554 0.7399
0.9087 23.0 10787 0.7843 0.7472 0.7352 0.7536 0.7345
0.9198 24.0 11256 0.7793 0.7504 0.7358 0.7554 0.7374
0.9162 25.0 11725 0.7791 0.752 0.7388 0.7570 0.7396

Framework versions

  • Transformers 4.43.3
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1