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bert-base-uncased-sst2-jpqd-ov-int8

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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: jpqd-bert-base-ft-sst2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE SST2
          type: glue
          config: sst2
          split: validation
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9162844036697247

jpqd-bert-base-ft-sst2

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.

It was compressed with NNCF following the Optimum JPQD text-classification example

It achieves the following results on the evaluation set:

  • Loss: 0.2798
  • Accuracy: 0.9163

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.392 0.12 250 0.4535 0.8888
0.4413 0.24 500 0.4671 0.8899
0.29 0.36 750 0.3285 0.9128
0.2851 0.48 1000 0.2498 0.9151
0.3717 0.59 1250 0.2037 0.9243
0.2467 0.71 1500 0.2840 0.9174
0.2114 0.83 1750 0.2239 0.9243
0.1777 0.95 2000 0.1968 0.9266
2.6501 1.07 2250 2.8219 0.9255
6.4768 1.19 2500 6.5765 0.8979
9.3594 1.31 2750 9.4648 0.8819
11.5481 1.43 3000 11.5391 0.8567
12.7541 1.54 3250 12.8359 0.8578
13.6184 1.66 3500 13.6519 0.8429
13.9171 1.78 3750 14.0734 0.8475
13.9601 1.9 4000 14.1024 0.8578
0.2701 2.02 4250 0.3354 0.9048
0.2689 2.14 4500 0.3320 0.9048
0.1775 2.26 4750 0.2838 0.9163
0.1648 2.38 5000 0.2842 0.9128
0.1316 2.49 5250 0.2750 0.9163
0.2349 2.61 5500 0.2405 0.9232
0.066 2.73 5750 0.2695 0.9174
0.1285 2.85 6000 0.3017 0.9094
0.1813 2.97 6250 0.3472 0.9106
0.078 3.09 6500 0.2915 0.9140
0.0886 3.21 6750 0.2853 0.9151
0.117 3.33 7000 0.2689 0.9186
0.0894 3.44 7250 0.2748 0.9174
0.1023 3.56 7500 0.3279 0.9094
0.0495 3.68 7750 0.2988 0.9151
0.0899 3.8 8000 0.2796 0.9174
0.1102 3.92 8250 0.2667 0.9163
0.061 4.04 8500 0.2837 0.9174
0.0594 4.16 8750 0.2766 0.9151
0.1062 4.28 9000 0.2777 0.9140
0.0751 4.39 9250 0.2690 0.9220
0.0386 4.51 9500 0.2668 0.9163
0.0284 4.63 9750 0.2812 0.9186
0.1016 4.75 10000 0.2825 0.9163
0.0507 4.87 10250 0.2805 0.9140
0.0709 4.99 10500 0.2855 0.9140

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.13.2