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best_model-yelp_polarity-16-42

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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best_model-yelp_polarity-16-42 / README.md
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metadata
license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-16-42
    results: []

best_model-yelp_polarity-16-42

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6276
  • Accuracy: 0.8125

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 0.7035 0.8438
No log 2.0 2 0.7063 0.8438
No log 3.0 3 0.7119 0.8438
No log 4.0 4 0.7198 0.8438
No log 5.0 5 0.7298 0.8125
No log 6.0 6 0.7415 0.8125
No log 7.0 7 0.7542 0.8125
No log 8.0 8 0.7678 0.8125
No log 9.0 9 0.7815 0.8125
0.4002 10.0 10 0.7947 0.8125
0.4002 11.0 11 0.8066 0.8125
0.4002 12.0 12 0.8164 0.8125
0.4002 13.0 13 0.8228 0.8125
0.4002 14.0 14 0.8259 0.8125
0.4002 15.0 15 0.8260 0.8125
0.4002 16.0 16 0.8231 0.8125
0.4002 17.0 17 0.8172 0.8125
0.4002 18.0 18 0.8084 0.8125
0.4002 19.0 19 0.7968 0.8125
0.3498 20.0 20 0.7826 0.8125
0.3498 21.0 21 0.7660 0.8125
0.3498 22.0 22 0.7474 0.8438
0.3498 23.0 23 0.7272 0.8438
0.3498 24.0 24 0.7053 0.8438
0.3498 25.0 25 0.6813 0.8438
0.3498 26.0 26 0.6547 0.8438
0.3498 27.0 27 0.6255 0.8438
0.3498 28.0 28 0.5952 0.8438
0.3498 29.0 29 0.5656 0.8125
0.2773 30.0 30 0.5407 0.8125
0.2773 31.0 31 0.5221 0.8125
0.2773 32.0 32 0.5096 0.8125
0.2773 33.0 33 0.5026 0.8125
0.2773 34.0 34 0.5080 0.8125
0.2773 35.0 35 0.5248 0.8125
0.2773 36.0 36 0.5517 0.8125
0.2773 37.0 37 0.5838 0.8125
0.2773 38.0 38 0.6122 0.8125
0.2773 39.0 39 0.6332 0.8125
0.1446 40.0 40 0.6455 0.8125
0.1446 41.0 41 0.6491 0.8125
0.1446 42.0 42 0.6449 0.8125
0.1446 43.0 43 0.6330 0.8125
0.1446 44.0 44 0.6121 0.8125
0.1446 45.0 45 0.5814 0.8125
0.1446 46.0 46 0.5390 0.8125
0.1446 47.0 47 0.4913 0.8125
0.1446 48.0 48 0.4598 0.8125
0.1446 49.0 49 0.4469 0.8438
0.066 50.0 50 0.4535 0.8438
0.066 51.0 51 0.4775 0.8125
0.066 52.0 52 0.5153 0.8125
0.066 53.0 53 0.5618 0.8125
0.066 54.0 54 0.6090 0.8125
0.066 55.0 55 0.6490 0.8125
0.066 56.0 56 0.6785 0.8125
0.066 57.0 57 0.6962 0.8125
0.066 58.0 58 0.7045 0.8125
0.066 59.0 59 0.7056 0.8125
0.0171 60.0 60 0.7001 0.8125
0.0171 61.0 61 0.6878 0.8125
0.0171 62.0 62 0.6688 0.8125
0.0171 63.0 63 0.6427 0.8125
0.0171 64.0 64 0.6110 0.8125
0.0171 65.0 65 0.5764 0.8125
0.0171 66.0 66 0.5422 0.8125
0.0171 67.0 67 0.5147 0.8125
0.0171 68.0 68 0.4976 0.8125
0.0171 69.0 69 0.4883 0.8125
0.0058 70.0 70 0.4876 0.8438
0.0058 71.0 71 0.4932 0.8438
0.0058 72.0 72 0.5018 0.8438
0.0058 73.0 73 0.5127 0.8125
0.0058 74.0 74 0.5251 0.8125
0.0058 75.0 75 0.5385 0.8125
0.0058 76.0 76 0.5517 0.8125
0.0058 77.0 77 0.5644 0.8125
0.0058 78.0 78 0.5758 0.8125
0.0058 79.0 79 0.5858 0.8125
0.0037 80.0 80 0.5941 0.8125
0.0037 81.0 81 0.6009 0.8125
0.0037 82.0 82 0.6064 0.8125
0.0037 83.0 83 0.6102 0.8125
0.0037 84.0 84 0.6119 0.8125
0.0037 85.0 85 0.6123 0.8125
0.0037 86.0 86 0.6108 0.8125
0.0037 87.0 87 0.6081 0.8125
0.0037 88.0 88 0.6040 0.8125
0.0037 89.0 89 0.5987 0.8125
0.0028 90.0 90 0.5923 0.8125
0.0028 91.0 91 0.5853 0.8125
0.0028 92.0 92 0.5779 0.8125
0.0028 93.0 93 0.5703 0.8125
0.0028 94.0 94 0.5627 0.8125
0.0028 95.0 95 0.5552 0.8125
0.0028 96.0 96 0.5481 0.8438
0.0028 97.0 97 0.5417 0.8438
0.0028 98.0 98 0.5365 0.8438
0.0028 99.0 99 0.5318 0.8438
0.0023 100.0 100 0.5280 0.8438
0.0023 101.0 101 0.5249 0.8438
0.0023 102.0 102 0.5220 0.8438
0.0023 103.0 103 0.5198 0.8438
0.0023 104.0 104 0.5180 0.8438
0.0023 105.0 105 0.5169 0.8438
0.0023 106.0 106 0.5167 0.8438
0.0023 107.0 107 0.5172 0.8438
0.0023 108.0 108 0.5184 0.8438
0.0023 109.0 109 0.5203 0.8438
0.0019 110.0 110 0.5224 0.8438
0.0019 111.0 111 0.5249 0.8438
0.0019 112.0 112 0.5278 0.8438
0.0019 113.0 113 0.5309 0.8438
0.0019 114.0 114 0.5343 0.8438
0.0019 115.0 115 0.5381 0.8438
0.0019 116.0 116 0.5422 0.8438
0.0019 117.0 117 0.5467 0.8438
0.0019 118.0 118 0.5514 0.8125
0.0019 119.0 119 0.5561 0.8125
0.0016 120.0 120 0.5609 0.8125
0.0016 121.0 121 0.5655 0.8125
0.0016 122.0 122 0.5703 0.8125
0.0016 123.0 123 0.5750 0.8125
0.0016 124.0 124 0.5796 0.8125
0.0016 125.0 125 0.5838 0.8125
0.0016 126.0 126 0.5877 0.8125
0.0016 127.0 127 0.5915 0.8125
0.0016 128.0 128 0.5950 0.8125
0.0016 129.0 129 0.5978 0.8125
0.0013 130.0 130 0.6002 0.8125
0.0013 131.0 131 0.6024 0.8125
0.0013 132.0 132 0.6045 0.8125
0.0013 133.0 133 0.6065 0.8125
0.0013 134.0 134 0.6082 0.8125
0.0013 135.0 135 0.6097 0.8125
0.0013 136.0 136 0.6113 0.8125
0.0013 137.0 137 0.6125 0.8125
0.0013 138.0 138 0.6136 0.8125
0.0013 139.0 139 0.6148 0.8125
0.0012 140.0 140 0.6158 0.8125
0.0012 141.0 141 0.6165 0.8125
0.0012 142.0 142 0.6172 0.8125
0.0012 143.0 143 0.6180 0.8125
0.0012 144.0 144 0.6190 0.8125
0.0012 145.0 145 0.6201 0.8125
0.0012 146.0 146 0.6215 0.8125
0.0012 147.0 147 0.6227 0.8125
0.0012 148.0 148 0.6239 0.8125
0.0012 149.0 149 0.6256 0.8125
0.001 150.0 150 0.6276 0.8125

Framework versions

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