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bert_base_lda_5_mrpc

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bert_base_lda_5_mrpc

This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_5 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6226
  • Accuracy: 0.6838
  • F1: 0.8122
  • Combined Score: 0.7480

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: 0.001
  • train_batch_size: 256
  • eval_batch_size: 256
  • seed: 10
  • optimizer: Use 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: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Combined Score
1.6067 1.0 15 0.6951 0.6838 0.8122 0.7480
0.6503 2.0 30 0.6270 0.6838 0.8122 0.7480
0.6289 3.0 45 0.6279 0.6838 0.8122 0.7480
0.6364 4.0 60 0.6244 0.6838 0.8122 0.7480
0.6368 5.0 75 0.6272 0.6838 0.8122 0.7480
0.6349 6.0 90 0.6252 0.6838 0.8122 0.7480
0.6311 7.0 105 0.6263 0.6838 0.8122 0.7480
0.6339 8.0 120 0.6240 0.6838 0.8122 0.7480
0.6296 9.0 135 0.6287 0.6838 0.8122 0.7480
0.6346 10.0 150 0.6260 0.6838 0.8122 0.7480
0.6359 11.0 165 0.6275 0.6838 0.8122 0.7480
0.6337 12.0 180 0.6242 0.6838 0.8122 0.7480
0.6302 13.0 195 0.6228 0.6838 0.8122 0.7480
0.6351 14.0 210 0.6258 0.6838 0.8122 0.7480
0.6324 15.0 225 0.6226 0.6838 0.8122 0.7480
0.6301 16.0 240 0.6243 0.6838 0.8122 0.7480
0.6316 17.0 255 0.6248 0.6838 0.8122 0.7480
0.63 18.0 270 0.6243 0.6838 0.8122 0.7480
0.6304 19.0 285 0.6230 0.6838 0.8122 0.7480
0.633 20.0 300 0.6250 0.6838 0.8122 0.7480

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.20.3
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Dataset used to train gokulsrinivasagan/bert_base_lda_5_mrpc

Evaluation results

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