pholarge

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

  • Loss: 0.2752
  • F1: 0.9361

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: 3e-05
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 40.0

Training results

Training Loss Epoch Step Validation Loss F1
0.3094 1.0 1033 0.2295 0.9327
0.2346 2.0 2066 0.2651 0.9280
0.2359 3.0 3099 0.2357 0.9338
0.1734 4.0 4132 0.2720 0.9277
0.1551 5.0 5165 0.2940 0.9350
0.1282 6.0 6198 0.2752 0.9361
0.1208 7.0 7231 0.3137 0.9347
0.1074 8.0 8264 0.2967 0.9355
0.2719 9.0 9297 0.8269 0.6410
0.8456 10.0 10330 0.8220 0.6410
0.84 11.0 11363 0.8212 0.6410
0.841 12.0 12396 0.8207 0.6410
0.8383 13.0 13429 0.8198 0.6410
0.8371 14.0 14462 0.8276 0.6410
0.8486 15.0 15495 0.8244 0.6410
0.844 16.0 16528 0.8391 0.6410
0.837 17.0 17561 0.8235 0.6410
0.8438 18.0 18594 0.8247 0.6410
0.8418 19.0 19627 0.8237 0.6410
0.8384 20.0 20660 0.8199 0.6410
0.8387 21.0 21693 0.8226 0.6410
0.8478 22.0 22726 0.8205 0.6410
0.8364 23.0 23759 0.8259 0.6410
0.8325 24.0 24792 0.8245 0.6410
0.8289 25.0 25825 0.8248 0.6410
0.8251 26.0 26858 0.8247 0.6410
0.824 27.0 27891 0.8214 0.6410
0.8197 28.0 28924 0.8282 0.6410
0.8241 29.0 29957 0.8340 0.6410
0.8285 30.0 30990 0.8360 0.6410
0.8169 31.0 32023 0.8401 0.6410
0.811 32.0 33056 0.8534 0.6410
0.8056 33.0 34089 0.8690 0.6410
0.8023 34.0 35122 0.8640 0.6410
0.8146 35.0 36155 0.8704 0.6410
0.8079 36.0 37188 0.8959 0.6410
0.8081 37.0 38221 0.8802 0.6410
0.8059 38.0 39254 0.8901 0.6410
0.8045 39.0 40287 0.8882 0.6410
0.8024 40.0 41320 0.8918 0.6410

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 3.1.0
  • Tokenizers 0.21.0
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