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bert-base-uncased-amazon_polarity

Text Classification
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PyTorch
bert
Generated from Trainer
sibyl
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - sibyl
datasets:
  - amazon_polarity
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-amazon_polarity
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_polarity
          type: amazon_polarity
          args: amazon_polarity
        metrics:
          - type: accuracy
            value: 0.94647
            name: Accuracy
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_polarity
          type: amazon_polarity
          config: amazon_polarity
          split: test
        metrics:
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            value: 0.9464875
            name: Accuracy
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          - type: auc
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            name: AUC
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bert-base-uncased-amazon_polarity

This model is a fine-tuned version of bert-base-uncased on the amazon_polarity dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2945
  • Accuracy: 0.9465

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: 1
  • 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: 1782000
  • training_steps: 17820000

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.7155 0.0 2000 0.7060 0.4622
0.7054 0.0 4000 0.6925 0.5165
0.6842 0.0 6000 0.6653 0.6116
0.6375 0.0 8000 0.5721 0.7909
0.4671 0.0 10000 0.3238 0.8770
0.3403 0.0 12000 0.3692 0.8861
0.4162 0.0 14000 0.4560 0.8908
0.4728 0.0 16000 0.5071 0.8980
0.5111 0.01 18000 0.5204 0.9015
0.4792 0.01 20000 0.5193 0.9076
0.544 0.01 22000 0.4835 0.9133
0.4745 0.01 24000 0.4689 0.9170
0.4403 0.01 26000 0.4778 0.9177
0.4405 0.01 28000 0.4754 0.9163
0.4375 0.01 30000 0.4808 0.9175
0.4628 0.01 32000 0.4340 0.9244
0.4488 0.01 34000 0.4162 0.9265
0.4608 0.01 36000 0.4031 0.9271
0.4478 0.01 38000 0.4502 0.9253
0.4237 0.01 40000 0.4087 0.9279
0.4601 0.01 42000 0.4133 0.9269
0.4153 0.01 44000 0.4230 0.9306
0.4096 0.01 46000 0.4108 0.9301
0.4348 0.01 48000 0.4138 0.9309
0.3787 0.01 50000 0.4066 0.9324
0.4172 0.01 52000 0.4812 0.9206
0.3897 0.02 54000 0.4013 0.9325
0.3787 0.02 56000 0.3837 0.9344
0.4253 0.02 58000 0.3925 0.9347
0.3959 0.02 60000 0.3907 0.9353
0.4402 0.02 62000 0.3708 0.9341
0.4115 0.02 64000 0.3477 0.9361
0.3876 0.02 66000 0.3634 0.9373
0.4286 0.02 68000 0.3778 0.9378
0.422 0.02 70000 0.3540 0.9361
0.3732 0.02 72000 0.3853 0.9378
0.3641 0.02 74000 0.3951 0.9386
0.3701 0.02 76000 0.3582 0.9388
0.4498 0.02 78000 0.3268 0.9375
0.3587 0.02 80000 0.3825 0.9401
0.4474 0.02 82000 0.3155 0.9391
0.3598 0.02 84000 0.3666 0.9388
0.389 0.02 86000 0.3745 0.9377
0.3625 0.02 88000 0.3776 0.9387
0.3511 0.03 90000 0.4275 0.9336
0.3428 0.03 92000 0.4301 0.9336
0.4042 0.03 94000 0.3547 0.9359
0.3583 0.03 96000 0.3763 0.9396
0.3887 0.03 98000 0.3213 0.9412
0.3915 0.03 100000 0.3557 0.9409
0.3378 0.03 102000 0.3627 0.9418
0.349 0.03 104000 0.3614 0.9402
0.3596 0.03 106000 0.3834 0.9381
0.3519 0.03 108000 0.3560 0.9421
0.3598 0.03 110000 0.3485 0.9419
0.3642 0.03 112000 0.3754 0.9395
0.3477 0.03 114000 0.3634 0.9426
0.4202 0.03 116000 0.3071 0.9427
0.3656 0.03 118000 0.3155 0.9441
0.3709 0.03 120000 0.2923 0.9433
0.374 0.03 122000 0.3272 0.9441
0.3142 0.03 124000 0.3348 0.9444
0.3452 0.04 126000 0.3603 0.9436
0.3365 0.04 128000 0.3339 0.9434
0.3353 0.04 130000 0.3471 0.9450
0.343 0.04 132000 0.3508 0.9418
0.3174 0.04 134000 0.3753 0.9436
0.3009 0.04 136000 0.3687 0.9422
0.3785 0.04 138000 0.3818 0.9396
0.3199 0.04 140000 0.3291 0.9438
0.4049 0.04 142000 0.3372 0.9454
0.3435 0.04 144000 0.3315 0.9459
0.3814 0.04 146000 0.3462 0.9401
0.359 0.04 148000 0.3981 0.9361
0.3552 0.04 150000 0.3226 0.9469
0.345 0.04 152000 0.3731 0.9384
0.3228 0.04 154000 0.2956 0.9471
0.3637 0.04 156000 0.2869 0.9477
0.349 0.04 158000 0.3331 0.9430
0.3374 0.04 160000 0.4159 0.9340
0.3718 0.05 162000 0.3241 0.9459
0.315 0.05 164000 0.3544 0.9391
0.3215 0.05 166000 0.3311 0.9451
0.3464 0.05 168000 0.3682 0.9453
0.3495 0.05 170000 0.3193 0.9469
0.305 0.05 172000 0.4132 0.9389
0.3479 0.05 174000 0.3465 0.947
0.3537 0.05 176000 0.3277 0.9449

Framework versions

  • Transformers 4.10.2
  • Pytorch 1.7.1
  • Datasets 1.12.1
  • Tokenizers 0.10.3