WillHeld / bert-base-cased-sst2

huggingface.co
Total runs: 18
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Model's Last Updated: September 16 2022
text-classification

Introduction of bert-base-cased-sst2

Model Details of bert-base-cased-sst2

bert-base-cased-sst2

This model is a fine-tuned version of bert-base-cased on the GLUE SST2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2345
  • Accuracy: 0.9140
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: 2e-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
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 10.0
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.6253 0.12 500 0.3641 0.8567
0.3189 0.24 1000 0.2656 0.8899
0.2701 0.36 1500 0.3463 0.8807
0.2533 0.48 2000 0.2409 0.9071
0.2436 0.59 2500 0.2345 0.9140
0.2155 0.71 3000 0.2926 0.9002
0.22 0.83 3500 0.2998 0.9094
0.2146 0.95 4000 0.2481 0.9140
0.1737 1.07 4500 0.2802 0.9128
0.1578 1.19 5000 0.3536 0.9083
0.1534 1.31 5500 0.4714 0.8830
0.1641 1.43 6000 0.3235 0.9128
0.1601 1.54 6500 0.3133 0.9094
0.1644 1.66 7000 0.3021 0.9071
0.1578 1.78 7500 0.3552 0.9094
0.1582 1.9 8000 0.2896 0.9106
0.1448 2.02 8500 0.3343 0.9232
0.0989 2.14 9000 0.3882 0.9048
0.1098 2.26 9500 0.3218 0.9037
0.1056 2.38 10000 0.3426 0.9140
0.112 2.49 10500 0.3631 0.9025
0.1066 2.61 11000 0.4084 0.9106
0.126 2.73 11500 0.3191 0.9117
0.12 2.85 12000 0.4091 0.9048
0.1092 2.97 12500 0.3602 0.9060
0.0826 3.09 13000 0.3571 0.9163
0.0603 3.21 13500 0.4021 0.9243
0.0636 3.33 14000 0.3893 0.9186
0.0775 3.44 14500 0.4373 0.9151
0.0842 3.56 15000 0.4100 0.9174
0.0902 3.68 15500 0.3878 0.9037
0.092 3.8 16000 0.3723 0.9140
0.0978 3.92 16500 0.3492 0.9163
0.0682 4.04 17000 0.4597 0.9209
0.0481 4.16 17500 0.4668 0.9186
0.0561 4.28 18000 0.4083 0.9209
0.0571 4.39 18500 0.4040 0.9174
0.0511 4.51 19000 0.4032 0.9197
0.062 4.63 19500 0.4090 0.9140
0.0618 4.75 20000 0.4150 0.9106
0.0599 4.87 20500 0.3623 0.9209
0.0614 4.99 21000 0.4421 0.9083
0.0385 5.11 21500 0.4328 0.9197
0.0331 5.23 22000 0.4569 0.9209
0.0343 5.34 22500 0.5130 0.9094
0.0389 5.46 23000 0.4741 0.9232
0.0413 5.58 23500 0.4654 0.9060
0.0444 5.7 24000 0.4888 0.9014
0.0406 5.82 24500 0.4085 0.9220
0.031 5.94 25000 0.4760 0.9197
0.037 6.06 25500 0.5403 0.9094
0.0239 6.18 26000 0.5945 0.9060
0.0267 6.29 26500 0.4595 0.9140
0.0338 6.41 27000 0.4923 0.9106
0.0293 6.53 27500 0.6128 0.8979
0.0253 6.65 28000 0.5428 0.9083
0.0296 6.77 28500 0.5244 0.9002
0.0279 6.89 29000 0.5732 0.9048
0.0321 7.01 29500 0.5824 0.9094
0.0179 7.13 30000 0.6336 0.9094
0.0177 7.24 30500 0.7145 0.9140
0.0262 7.36 31000 0.5504 0.9083
0.0182 7.48 31500 0.5924 0.9071
0.0187 7.6 32000 0.5613 0.9151
0.012 7.72 32500 0.6129 0.9083
0.021 7.84 33000 0.5698 0.9106
0.024 7.96 33500 0.6231 0.9083
0.0136 8.08 34000 0.7155 0.9117
0.0088 8.19 34500 0.7918 0.9060
0.0129 8.31 35000 0.6727 0.9094
0.0113 8.43 35500 0.6531 0.9117
0.0141 8.55 36000 0.7040 0.9037
0.0111 8.67 36500 0.6551 0.9094
0.0111 8.79 37000 0.6928 0.9071
0.0116 8.91 37500 0.6313 0.9094
0.0107 9.03 38000 0.7104 0.9094
0.006 9.14 38500 0.7446 0.9117
0.0048 9.26 39000 0.7537 0.9140
0.0099 9.38 39500 0.7715 0.9140
0.0067 9.5 40000 0.7633 0.9117
0.0037 9.62 40500 0.7669 0.9128
0.006 9.74 41000 0.7714 0.9128
0.0063 9.86 41500 0.8020 0.9106
0.0107 9.98 42000 0.7985 0.9117
Framework versions
  • Transformers 4.21.3
  • Pytorch 1.7.1
  • Datasets 1.18.3
  • Tokenizers 0.11.6

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