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mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_stsb

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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - spearmanr
model-index:
  - name: mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_stsb
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE STSB
          type: glue
          config: stsb
          split: validation
          args: stsb
        metrics:
          - name: Spearmanr
            type: spearmanr
            value: 0.8642221596976783

mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_stsb

This model is a fine-tuned version of gokuls/mobilebert_sa_pre-training-complete on the GLUE STSB dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2919
  • Pearson: 0.8665
  • Spearmanr: 0.8642
  • Combined Score: 0.8654

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-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Pearson Spearmanr Combined Score
1.1501 1.0 45 0.4726 0.7774 0.7922 0.7848
0.364 2.0 90 0.3480 0.8457 0.8455 0.8456
0.259 3.0 135 0.3156 0.8582 0.8590 0.8586
0.2054 4.0 180 0.4231 0.8551 0.8549 0.8550
0.1629 5.0 225 0.3245 0.8668 0.8654 0.8661
0.1263 6.0 270 0.3192 0.8649 0.8625 0.8637
0.1021 7.0 315 0.3337 0.8655 0.8629 0.8642
0.0841 8.0 360 0.3061 0.8601 0.8577 0.8589
0.0713 9.0 405 0.3600 0.8576 0.8555 0.8566
0.0587 10.0 450 0.3135 0.8620 0.8600 0.8610
0.0488 11.0 495 0.3006 0.8641 0.8620 0.8631
0.0441 12.0 540 0.3308 0.8645 0.8621 0.8633
0.0385 13.0 585 0.3468 0.8620 0.8601 0.8610
0.0346 14.0 630 0.3175 0.8658 0.8634 0.8646
0.0298 15.0 675 0.2919 0.8665 0.8642 0.8654
0.0299 16.0 720 0.3103 0.8649 0.8628 0.8639
0.0263 17.0 765 0.3325 0.8620 0.8599 0.8609
0.0237 18.0 810 0.3092 0.8636 0.8611 0.8623
0.0213 19.0 855 0.3169 0.8653 0.8631 0.8642
0.0196 20.0 900 0.2985 0.8647 0.8624 0.8636

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.9.0
  • Tokenizers 0.13.2