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best_model-yelp_polarity-16-100

Text Classification
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PyTorch
albert
Generated from Trainer
Inference Endpoints
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metadata
license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-16-100
    results: []

best_model-yelp_polarity-16-100

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

  • Loss: 0.3649
  • Accuracy: 0.9375

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 1 0.4545 0.9375
No log 2.0 2 0.4554 0.9375
No log 3.0 3 0.4547 0.9375
No log 4.0 4 0.4527 0.9375
No log 5.0 5 0.4500 0.9375
No log 6.0 6 0.4459 0.9375
No log 7.0 7 0.4399 0.9375
No log 8.0 8 0.4325 0.9375
No log 9.0 9 0.4229 0.9375
0.0615 10.0 10 0.4163 0.9375
0.0615 11.0 11 0.4128 0.9375
0.0615 12.0 12 0.4064 0.9375
0.0615 13.0 13 0.3967 0.9375
0.0615 14.0 14 0.3834 0.9375
0.0615 15.0 15 0.3664 0.9375
0.0615 16.0 16 0.3437 0.9375
0.0615 17.0 17 0.3272 0.9375
0.0615 18.0 18 0.3177 0.9375
0.0615 19.0 19 0.3141 0.9375
0.0434 20.0 20 0.3169 0.9375
0.0434 21.0 21 0.3263 0.9375
0.0434 22.0 22 0.3365 0.9375
0.0434 23.0 23 0.3472 0.9375
0.0434 24.0 24 0.3639 0.9375
0.0434 25.0 25 0.3799 0.9375
0.0434 26.0 26 0.3938 0.9375
0.0434 27.0 27 0.4059 0.9375
0.0434 28.0 28 0.4103 0.9375
0.0434 29.0 29 0.4072 0.9375
0.0006 30.0 30 0.4046 0.9375
0.0006 31.0 31 0.4023 0.9375
0.0006 32.0 32 0.4003 0.9375
0.0006 33.0 33 0.3990 0.9375
0.0006 34.0 34 0.3979 0.9375
0.0006 35.0 35 0.3969 0.9375
0.0006 36.0 36 0.3961 0.9375
0.0006 37.0 37 0.3955 0.9375
0.0006 38.0 38 0.3951 0.9375
0.0006 39.0 39 0.3954 0.9375
0.0003 40.0 40 0.3960 0.9375
0.0003 41.0 41 0.3961 0.9375
0.0003 42.0 42 0.3949 0.9375
0.0003 43.0 43 0.3912 0.9375
0.0003 44.0 44 0.3875 0.9375
0.0003 45.0 45 0.3851 0.9375
0.0003 46.0 46 0.3833 0.9375
0.0003 47.0 47 0.3822 0.9375
0.0003 48.0 48 0.3812 0.9375
0.0003 49.0 49 0.3807 0.9375
0.0003 50.0 50 0.3805 0.9375
0.0003 51.0 51 0.3807 0.9375
0.0003 52.0 52 0.3812 0.9375
0.0003 53.0 53 0.3820 0.9375
0.0003 54.0 54 0.3830 0.9375
0.0003 55.0 55 0.3841 0.9375
0.0003 56.0 56 0.3859 0.9375
0.0003 57.0 57 0.3885 0.9375
0.0003 58.0 58 0.3923 0.9375
0.0003 59.0 59 0.3958 0.9375
0.0003 60.0 60 0.3992 0.9375
0.0003 61.0 61 0.4026 0.9375
0.0003 62.0 62 0.4059 0.9375
0.0003 63.0 63 0.4093 0.9375
0.0003 64.0 64 0.4125 0.9375
0.0003 65.0 65 0.4152 0.9375
0.0003 66.0 66 0.4179 0.9375
0.0003 67.0 67 0.4207 0.9375
0.0003 68.0 68 0.4234 0.9375
0.0003 69.0 69 0.4291 0.9375
0.0002 70.0 70 0.4345 0.9375
0.0002 71.0 71 0.4392 0.9375
0.0002 72.0 72 0.4434 0.9375
0.0002 73.0 73 0.4568 0.9375
0.0002 74.0 74 0.4678 0.9375
0.0002 75.0 75 0.4775 0.9375
0.0002 76.0 76 0.4831 0.9375
0.0002 77.0 77 0.4880 0.9375
0.0002 78.0 78 0.4925 0.9375
0.0002 79.0 79 0.4964 0.9375
0.0002 80.0 80 0.4984 0.9375
0.0002 81.0 81 0.4999 0.9375
0.0002 82.0 82 0.5013 0.9375
0.0002 83.0 83 0.5027 0.9375
0.0002 84.0 84 0.5039 0.9375
0.0002 85.0 85 0.5050 0.9375
0.0002 86.0 86 0.5061 0.9375
0.0002 87.0 87 0.5071 0.9375
0.0002 88.0 88 0.5081 0.9375
0.0002 89.0 89 0.5090 0.9375
0.0002 90.0 90 0.5099 0.9375
0.0002 91.0 91 0.5102 0.9375
0.0002 92.0 92 0.5105 0.9375
0.0002 93.0 93 0.5109 0.9375
0.0002 94.0 94 0.5114 0.9375
0.0002 95.0 95 0.5115 0.9375
0.0002 96.0 96 0.5117 0.9375
0.0002 97.0 97 0.4927 0.9375
0.0002 98.0 98 0.4685 0.9375
0.0002 99.0 99 0.4380 0.9375
0.0003 100.0 100 0.4010 0.9375
0.0003 101.0 101 0.3594 0.9375
0.0003 102.0 102 0.3201 0.9375
0.0003 103.0 103 0.2908 0.9375
0.0003 104.0 104 0.2745 0.9688
0.0003 105.0 105 0.2665 0.9688
0.0003 106.0 106 0.2624 0.9688
0.0003 107.0 107 0.2597 0.9688
0.0003 108.0 108 0.2575 0.9688
0.0003 109.0 109 0.2558 0.9688
0.0002 110.0 110 0.2544 0.9688
0.0002 111.0 111 0.2531 0.9688
0.0002 112.0 112 0.2521 0.9688
0.0002 113.0 113 0.2513 0.9688
0.0002 114.0 114 0.2506 0.9688
0.0002 115.0 115 0.2502 0.9688
0.0002 116.0 116 0.2501 0.9688
0.0002 117.0 117 0.2500 0.9688
0.0002 118.0 118 0.2501 0.9688
0.0002 119.0 119 0.2503 0.9688
0.0001 120.0 120 0.2505 0.9688
0.0001 121.0 121 0.2532 0.9688
0.0001 122.0 122 0.2560 0.9688
0.0001 123.0 123 0.2585 0.9688
0.0001 124.0 124 0.2608 0.9688
0.0001 125.0 125 0.2630 0.9688
0.0001 126.0 126 0.2654 0.9688
0.0001 127.0 127 0.2676 0.9688
0.0001 128.0 128 0.2696 0.9688
0.0001 129.0 129 0.2717 0.9688
0.0002 130.0 130 0.2737 0.9688
0.0002 131.0 131 0.2759 0.9688
0.0002 132.0 132 0.2783 0.9688
0.0002 133.0 133 0.2808 0.9688
0.0002 134.0 134 0.2837 0.9688
0.0002 135.0 135 0.2871 0.9688
0.0002 136.0 136 0.2908 0.9688
0.0002 137.0 137 0.2950 0.9688
0.0002 138.0 138 0.2995 0.9688
0.0002 139.0 139 0.3043 0.9375
0.0001 140.0 140 0.3094 0.9375
0.0001 141.0 141 0.3147 0.9375
0.0001 142.0 142 0.3201 0.9375
0.0001 143.0 143 0.3257 0.9375
0.0001 144.0 144 0.3316 0.9375
0.0001 145.0 145 0.3375 0.9375
0.0001 146.0 146 0.3434 0.9375
0.0001 147.0 147 0.3492 0.9375
0.0001 148.0 148 0.3547 0.9375
0.0001 149.0 149 0.3599 0.9375
0.0001 150.0 150 0.3649 0.9375

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.4.0
  • Tokenizers 0.13.3