WillHeld / roberta-base-sst2

huggingface.co
Total runs: 118
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Model's Last Updated: September 18 2022
text-classification

Introduction of roberta-base-sst2

Model Details of roberta-base-sst2

roberta-base-sst2

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

  • Loss: 0.1952
  • Accuracy: 0.9323
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
0.575 0.12 500 0.2665 0.9071
0.2989 0.24 1000 0.2088 0.9220
0.2725 0.36 1500 0.2560 0.9243
0.2814 0.48 2000 0.2016 0.9266
0.2586 0.59 2500 0.2293 0.9174
0.2536 0.71 3000 0.2340 0.9323
0.2494 0.83 3500 0.1952 0.9323
0.2396 0.95 4000 0.2494 0.9323
0.2123 1.07 4500 0.2187 0.9381
0.2042 1.19 5000 0.2812 0.9151
0.2083 1.31 5500 0.2739 0.9346
0.2041 1.43 6000 0.2087 0.9381
0.1969 1.54 6500 0.2590 0.9255
0.1982 1.66 7000 0.2445 0.9300
0.1943 1.78 7500 0.2798 0.9266
0.1848 1.9 8000 0.2844 0.9312
0.1788 2.02 8500 0.2998 0.9255
0.1623 2.14 9000 0.2696 0.9392
0.1499 2.26 9500 0.2533 0.9278
0.1426 2.38 10000 0.2971 0.9300
0.1479 2.49 10500 0.2596 0.9358
0.1405 2.61 11000 0.2945 0.9255
0.1577 2.73 11500 0.4061 0.9002
0.1521 2.85 12000 0.2724 0.9335
0.1426 2.97 12500 0.2712 0.9427
0.1206 3.09 13000 0.2954 0.9358
0.1074 3.21 13500 0.2653 0.9392
0.112 3.33 14000 0.2778 0.9346
0.1147 3.44 14500 0.3705 0.9312
0.1196 3.56 15000 0.2890 0.9346
0.1159 3.68 15500 0.3449 0.9266
0.119 3.8 16000 0.3207 0.9335
0.1268 3.92 16500 0.3235 0.9312
0.1074 4.04 17000 0.3650 0.9335
0.0805 4.16 17500 0.3338 0.9381
0.0838 4.28 18000 0.4302 0.9209
0.0848 4.39 18500 0.4096 0.9323
0.0922 4.51 19000 0.3332 0.9369
0.091 4.63 19500 0.3024 0.9438
0.0977 4.75 20000 0.2674 0.9495
0.0897 4.87 20500 0.3993 0.9300
0.1013 4.99 21000 0.3227 0.9289
0.0671 5.11 21500 0.3374 0.9427
0.0671 5.23 22000 0.4108 0.9278
0.0652 5.34 22500 0.3550 0.9381
0.0664 5.46 23000 0.3398 0.9358
0.0742 5.58 23500 0.3286 0.9381
0.0758 5.7 24000 0.3276 0.9312
0.075 5.82 24500 0.3202 0.9369
0.0686 5.94 25000 0.3481 0.9415
0.0729 6.06 25500 0.3816 0.9335
0.0568 6.18 26000 0.3132 0.9381
0.0529 6.29 26500 0.3757 0.9300
0.0506 6.41 27000 0.3396 0.9381
0.0476 6.53 27500 0.3642 0.9404
0.0555 6.65 28000 0.3430 0.9404
0.0574 6.77 28500 0.3401 0.9392
0.0524 6.89 29000 0.3378 0.9346
0.0492 7.01 29500 0.3833 0.9381
0.039 7.13 30000 0.3347 0.9346
0.0411 7.24 30500 0.4404 0.9335
0.0412 7.36 31000 0.3618 0.9381
0.0477 7.48 31500 0.3806 0.9381
0.0435 7.6 32000 0.3912 0.9335
0.0443 7.72 32500 0.3900 0.9392
0.0421 7.84 33000 0.4152 0.9369
0.0495 7.96 33500 0.3832 0.9289
0.0293 8.08 34000 0.4427 0.9346
0.0253 8.19 34500 0.4425 0.9381
0.0407 8.31 35000 0.4102 0.9358
0.0311 8.43 35500 0.4447 0.9369
0.0291 8.55 36000 0.4612 0.9346
0.035 8.67 36500 0.4241 0.9346
0.0381 8.79 37000 0.4198 0.9312
0.0234 8.91 37500 0.4345 0.9369
0.0311 9.03 38000 0.4558 0.9312
0.028 9.14 38500 0.4245 0.9381
0.0213 9.26 39000 0.4462 0.9381
0.0276 9.38 39500 0.4210 0.9381
0.0183 9.5 40000 0.4310 0.9404
0.0184 9.62 40500 0.4437 0.9404
0.0296 9.74 41000 0.4311 0.9392
0.019 9.86 41500 0.4244 0.9415
0.0245 9.98 42000 0.4270 0.9415
Framework versions
  • Transformers 4.21.3
  • Pytorch 1.7.1
  • Datasets 1.18.3
  • Tokenizers 0.11.6

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