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amazonPolarity_BERT_5E

Text Classification Transformers PyTorch
bert generated_from_trainer Eval Results Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - amazon_polarity
metrics:
  - accuracy
model-index:
  - name: amazonPolarity_BERT_5E
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: amazon_polarity
          type: amazon_polarity
          config: amazon_polarity
          split: train
          args: amazon_polarity
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9066666666666666

amazonPolarity_BERT_5E

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

  • Loss: 0.4402
  • Accuracy: 0.9067

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: 1e-05
  • train_batch_size: 16
  • 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

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.7011 0.03 50 0.6199 0.7
0.6238 0.05 100 0.4710 0.8133
0.4478 0.08 150 0.3249 0.8733
0.3646 0.11 200 0.3044 0.86
0.3244 0.13 250 0.2548 0.86
0.2734 0.16 300 0.2666 0.88
0.2784 0.19 350 0.2416 0.88
0.2706 0.21 400 0.2660 0.88
0.2368 0.24 450 0.2522 0.8867
0.2449 0.27 500 0.3135 0.88
0.262 0.29 550 0.2718 0.8733
0.2111 0.32 600 0.2494 0.8933
0.2459 0.35 650 0.2468 0.8867
0.2264 0.37 700 0.3049 0.8667
0.2572 0.4 750 0.2054 0.8933
0.1749 0.43 800 0.3489 0.86
0.2423 0.45 850 0.2142 0.8933
0.1931 0.48 900 0.2096 0.9067
0.2444 0.51 950 0.3404 0.8733
0.2666 0.53 1000 0.2378 0.9067
0.2311 0.56 1050 0.2416 0.9067
0.2269 0.59 1100 0.3188 0.8733
0.2143 0.61 1150 0.2343 0.9
0.2181 0.64 1200 0.2606 0.8667
0.2151 0.67 1250 0.1888 0.9133
0.2694 0.69 1300 0.3982 0.8467
0.2408 0.72 1350 0.1978 0.9067
0.2043 0.75 1400 0.2125 0.9
0.2081 0.77 1450 0.2680 0.8933
0.2361 0.8 1500 0.3723 0.8467
0.2503 0.83 1550 0.3427 0.8733
0.1983 0.85 1600 0.2525 0.9067
0.1947 0.88 1650 0.2427 0.9133
0.2411 0.91 1700 0.2448 0.9
0.2381 0.93 1750 0.3354 0.88
0.1852 0.96 1800 0.3078 0.8667
0.2427 0.99 1850 0.2408 0.9
0.1582 1.01 1900 0.2698 0.9133
0.159 1.04 1950 0.3383 0.9
0.1833 1.07 2000 0.2849 0.9
0.1257 1.09 2050 0.5376 0.8667
0.1513 1.12 2100 0.4469 0.88
0.1869 1.15 2150 0.3415 0.8933
0.1342 1.17 2200 0.3021 0.8867
0.1404 1.2 2250 0.3619 0.88
0.1576 1.23 2300 0.2815 0.9
0.1419 1.25 2350 0.4351 0.8867
0.1491 1.28 2400 0.3025 0.9133
0.1914 1.31 2450 0.3011 0.9067
0.1265 1.33 2500 0.3953 0.88
0.128 1.36 2550 0.2557 0.9333
0.1631 1.39 2600 0.2226 0.9333
0.1019 1.41 2650 0.3638 0.9133
0.1551 1.44 2700 0.3591 0.9
0.1853 1.47 2750 0.5005 0.8733
0.1578 1.49 2800 0.2662 0.92
0.1522 1.52 2850 0.2545 0.9267
0.1188 1.55 2900 0.3874 0.88
0.1638 1.57 2950 0.3003 0.92
0.1583 1.6 3000 0.2702 0.92
0.1844 1.63 3050 0.2183 0.9333
0.1365 1.65 3100 0.3322 0.8933
0.1683 1.68 3150 0.2069 0.9467
0.168 1.71 3200 0.4046 0.8667
0.1907 1.73 3250 0.3411 0.8933
0.1695 1.76 3300 0.1992 0.9333
0.1851 1.79 3350 0.2370 0.92
0.1302 1.81 3400 0.3058 0.9133
0.1353 1.84 3450 0.3134 0.9067
0.1428 1.87 3500 0.3767 0.8667
0.1642 1.89 3550 0.3239 0.8867
0.1319 1.92 3600 0.4725 0.86
0.1714 1.95 3650 0.3115 0.8867
0.1265 1.97 3700 0.3621 0.8867
0.1222 2.0 3750 0.3665 0.8933
0.0821 2.03 3800 0.2482 0.9133
0.1136 2.05 3850 0.3244 0.9
0.0915 2.08 3900 0.4745 0.8733
0.0967 2.11 3950 0.2346 0.94
0.0962 2.13 4000 0.3139 0.92
0.1001 2.16 4050 0.2944 0.9267
0.086 2.19 4100 0.5542 0.86
0.0588 2.21 4150 0.4377 0.9
0.1056 2.24 4200 0.3540 0.9133
0.0899 2.27 4250 0.5661 0.8733
0.0737 2.29 4300 0.5683 0.8733
0.1152 2.32 4350 0.2997 0.9333
0.0852 2.35 4400 0.5055 0.8933
0.1114 2.37 4450 0.3099 0.92
0.0821 2.4 4500 0.3026 0.9267
0.0698 2.43 4550 0.3250 0.92
0.1123 2.45 4600 0.3674 0.9
0.1196 2.48 4650 0.4539 0.8733
0.0617 2.51 4700 0.3446 0.92
0.0939 2.53 4750 0.3302 0.92
0.1114 2.56 4800 0.5149 0.8733
0.1154 2.59 4850 0.4935 0.8867
0.1495 2.61 4900 0.4706 0.8933
0.0858 2.64 4950 0.4048 0.9
0.0767 2.67 5000 0.3849 0.9133
0.0569 2.69 5050 0.5491 0.8867
0.1058 2.72 5100 0.5872 0.8733
0.0899 2.75 5150 0.3159 0.92
0.0757 2.77 5200 0.5861 0.8733
0.1305 2.8 5250 0.3633 0.9133
0.1027 2.83 5300 0.3972 0.9133
0.1259 2.85 5350 0.4197 0.8933
0.1255 2.88 5400 0.4583 0.8867
0.0981 2.91 5450 0.4657 0.8933
0.0736 2.93 5500 0.4036 0.9133
0.116 2.96 5550 0.3026 0.9067
0.0692 2.99 5600 0.3409 0.9133
0.0721 3.01 5650 0.5598 0.8733
0.052 3.04 5700 0.4130 0.9133
0.0661 3.07 5750 0.2589 0.9333
0.0667 3.09 5800 0.4484 0.9067
0.0599 3.12 5850 0.4883 0.9
0.0406 3.15 5900 0.4516 0.9067
0.0837 3.17 5950 0.3394 0.9267
0.0636 3.2 6000 0.4649 0.8867
0.0861 3.23 6050 0.5046 0.8933
0.0667 3.25 6100 0.3252 0.92
0.0401 3.28 6150 0.2771 0.94
0.0998 3.31 6200 0.4509 0.9
0.0209 3.33 6250 0.4666 0.8933
0.0747 3.36 6300 0.5430 0.8867
0.0678 3.39 6350 0.4050 0.9067
0.0685 3.41 6400 0.3738 0.92
0.0654 3.44 6450 0.4486 0.9
0.0496 3.47 6500 0.4386 0.9067
0.0379 3.49 6550 0.4547 0.9067
0.0897 3.52 6600 0.4197 0.9133
0.0729 3.55 6650 0.2855 0.9333
0.0515 3.57 6700 0.4459 0.9067
0.0588 3.6 6750 0.3627 0.92
0.0724 3.63 6800 0.4060 0.9267
0.0607 3.65 6850 0.4505 0.9133
0.0252 3.68 6900 0.5465 0.8933
0.0594 3.71 6950 0.4786 0.9067
0.0743 3.73 7000 0.4163 0.9267
0.0506 3.76 7050 0.3801 0.92
0.0548 3.79 7100 0.3557 0.9267
0.0932 3.81 7150 0.4278 0.9133
0.0643 3.84 7200 0.4673 0.9
0.0631 3.87 7250 0.3611 0.92
0.0793 3.89 7300 0.3956 0.9067
0.0729 3.92 7350 0.6630 0.8733
0.0552 3.95 7400 0.4259 0.8867
0.0432 3.97 7450 0.3615 0.92
0.0697 4.0 7500 0.5116 0.88
0.0463 4.03 7550 0.3334 0.94
0.046 4.05 7600 0.4704 0.8867
0.0371 4.08 7650 0.3323 0.94
0.0809 4.11 7700 0.3503 0.92
0.0285 4.13 7750 0.3360 0.92
0.0469 4.16 7800 0.3365 0.9333
0.041 4.19 7850 0.5726 0.88
0.0447 4.21 7900 0.4564 0.9067
0.0144 4.24 7950 0.5521 0.8867
0.0511 4.27 8000 0.5661 0.88
0.0481 4.29 8050 0.3445 0.94
0.036 4.32 8100 0.3247 0.94
0.0662 4.35 8150 0.3647 0.9333
0.051 4.37 8200 0.5024 0.9
0.0546 4.4 8250 0.4737 0.8933
0.0526 4.43 8300 0.4067 0.92
0.0291 4.45 8350 0.3862 0.9267
0.0292 4.48 8400 0.5101 0.9
0.0426 4.51 8450 0.4207 0.92
0.0771 4.53 8500 0.5525 0.8867
0.0668 4.56 8550 0.4487 0.9067
0.0585 4.59 8600 0.3574 0.9267
0.0375 4.61 8650 0.3980 0.92
0.0508 4.64 8700 0.4064 0.92
0.0334 4.67 8750 0.3031 0.94
0.0257 4.69 8800 0.3340 0.9333
0.0165 4.72 8850 0.4011 0.92
0.0553 4.75 8900 0.4243 0.9133
0.0597 4.77 8950 0.3685 0.9267
0.0407 4.8 9000 0.4262 0.9133
0.032 4.83 9050 0.4080 0.9133
0.0573 4.85 9100 0.4416 0.9133
0.0308 4.88 9150 0.4397 0.9133
0.0494 4.91 9200 0.4476 0.9067
0.015 4.93 9250 0.4419 0.9067
0.0443 4.96 9300 0.4347 0.9133
0.0479 4.99 9350 0.4402 0.9067

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.0
  • Datasets 2.6.1
  • Tokenizers 0.13.1