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best_model-yelp_polarity-16-21

Text Classification
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PyTorch
albert
Generated from Trainer
Inference Endpoints
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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-16-21
    results: []

best_model-yelp_polarity-16-21

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: 0.8234
  • Accuracy: 0.75

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 0.8218 0.625
No log 2.0 2 0.8206 0.625
No log 3.0 3 0.8183 0.625
No log 4.0 4 0.8150 0.625
No log 5.0 5 0.8107 0.625
No log 6.0 6 0.8057 0.625
No log 7.0 7 0.8001 0.6562
No log 8.0 8 0.7944 0.6875
No log 9.0 9 0.7887 0.7188
0.5647 10.0 10 0.7834 0.7188
0.5647 11.0 11 0.7784 0.7188
0.5647 12.0 12 0.7738 0.7188
0.5647 13.0 13 0.7695 0.7188
0.5647 14.0 14 0.7651 0.7188
0.5647 15.0 15 0.7606 0.7188
0.5647 16.0 16 0.7558 0.7188
0.5647 17.0 17 0.7506 0.7188
0.5647 18.0 18 0.7451 0.7188
0.5647 19.0 19 0.7392 0.7188
0.472 20.0 20 0.7329 0.7188
0.472 21.0 21 0.7262 0.7188
0.472 22.0 22 0.7190 0.7188
0.472 23.0 23 0.7112 0.75
0.472 24.0 24 0.7029 0.75
0.472 25.0 25 0.6941 0.75
0.472 26.0 26 0.6847 0.75
0.472 27.0 27 0.6749 0.75
0.472 28.0 28 0.6647 0.75
0.472 29.0 29 0.6545 0.75
0.3267 30.0 30 0.6445 0.75
0.3267 31.0 31 0.6350 0.6562
0.3267 32.0 32 0.6261 0.6562
0.3267 33.0 33 0.6177 0.6875
0.3267 34.0 34 0.6100 0.6875
0.3267 35.0 35 0.6031 0.6875
0.3267 36.0 36 0.5973 0.6875
0.3267 37.0 37 0.5926 0.7188
0.3267 38.0 38 0.5895 0.7188
0.3267 39.0 39 0.5869 0.7188
0.1824 40.0 40 0.5842 0.75
0.1824 41.0 41 0.5796 0.75
0.1824 42.0 42 0.5730 0.75
0.1824 43.0 43 0.5651 0.75
0.1824 44.0 44 0.5555 0.75
0.1824 45.0 45 0.5466 0.7812
0.1824 46.0 46 0.5408 0.7812
0.1824 47.0 47 0.5379 0.7812
0.1824 48.0 48 0.5386 0.7812
0.1824 49.0 49 0.5419 0.7812
0.0885 50.0 50 0.5482 0.7812
0.0885 51.0 51 0.5568 0.7812
0.0885 52.0 52 0.5662 0.7812
0.0885 53.0 53 0.5761 0.7812
0.0885 54.0 54 0.5834 0.7812
0.0885 55.0 55 0.5897 0.8125
0.0885 56.0 56 0.5929 0.8125
0.0885 57.0 57 0.5930 0.8125
0.0885 58.0 58 0.5905 0.7812
0.0885 59.0 59 0.5869 0.7812
0.0497 60.0 60 0.5830 0.7812
0.0497 61.0 61 0.5795 0.75
0.0497 62.0 62 0.5776 0.75
0.0497 63.0 63 0.5777 0.75
0.0497 64.0 64 0.5800 0.75
0.0497 65.0 65 0.5832 0.75
0.0497 66.0 66 0.5887 0.75
0.0497 67.0 67 0.5962 0.7812
0.0497 68.0 68 0.6062 0.7812
0.0497 69.0 69 0.6192 0.75
0.0306 70.0 70 0.6332 0.75
0.0306 71.0 71 0.6475 0.75
0.0306 72.0 72 0.6610 0.75
0.0306 73.0 73 0.6726 0.75
0.0306 74.0 74 0.6824 0.75
0.0306 75.0 75 0.6910 0.75
0.0306 76.0 76 0.6989 0.75
0.0306 77.0 77 0.7058 0.75
0.0306 78.0 78 0.7122 0.75
0.0306 79.0 79 0.7179 0.7188
0.0175 80.0 80 0.7230 0.7188
0.0175 81.0 81 0.7281 0.7188
0.0175 82.0 82 0.7331 0.7188
0.0175 83.0 83 0.7385 0.7188
0.0175 84.0 84 0.7428 0.7188
0.0175 85.0 85 0.7462 0.7188
0.0175 86.0 86 0.7491 0.75
0.0175 87.0 87 0.7520 0.75
0.0175 88.0 88 0.7544 0.75
0.0175 89.0 89 0.7566 0.75
0.0111 90.0 90 0.7584 0.75
0.0111 91.0 91 0.7604 0.75
0.0111 92.0 92 0.7622 0.75
0.0111 93.0 93 0.7641 0.75
0.0111 94.0 94 0.7665 0.75
0.0111 95.0 95 0.7693 0.75
0.0111 96.0 96 0.7724 0.75
0.0111 97.0 97 0.7757 0.75
0.0111 98.0 98 0.7792 0.75
0.0111 99.0 99 0.7828 0.75
0.0078 100.0 100 0.7868 0.75
0.0078 101.0 101 0.7911 0.75
0.0078 102.0 102 0.7959 0.75
0.0078 103.0 103 0.8010 0.75
0.0078 104.0 104 0.8059 0.75
0.0078 105.0 105 0.8106 0.75
0.0078 106.0 106 0.8150 0.75
0.0078 107.0 107 0.8193 0.75
0.0078 108.0 108 0.8230 0.75
0.0078 109.0 109 0.8263 0.75
0.0061 110.0 110 0.8290 0.75
0.0061 111.0 111 0.8312 0.75
0.0061 112.0 112 0.8328 0.75
0.0061 113.0 113 0.8339 0.75
0.0061 114.0 114 0.8345 0.75
0.0061 115.0 115 0.8348 0.75
0.0061 116.0 116 0.8347 0.75
0.0061 117.0 117 0.8338 0.75
0.0061 118.0 118 0.8329 0.75
0.0061 119.0 119 0.8322 0.75
0.0048 120.0 120 0.8315 0.75
0.0048 121.0 121 0.8308 0.75
0.0048 122.0 122 0.8301 0.75
0.0048 123.0 123 0.8296 0.75
0.0048 124.0 124 0.8294 0.75
0.0048 125.0 125 0.8296 0.75
0.0048 126.0 126 0.8299 0.75
0.0048 127.0 127 0.8302 0.75
0.0048 128.0 128 0.8302 0.75
0.0048 129.0 129 0.8304 0.75
0.0039 130.0 130 0.8306 0.75
0.0039 131.0 131 0.8305 0.75
0.0039 132.0 132 0.8301 0.75
0.0039 133.0 133 0.8296 0.7812
0.0039 134.0 134 0.8292 0.7812
0.0039 135.0 135 0.8283 0.7812
0.0039 136.0 136 0.8272 0.7812
0.0039 137.0 137 0.8259 0.7812
0.0039 138.0 138 0.8247 0.7812
0.0039 139.0 139 0.8237 0.75
0.0032 140.0 140 0.8228 0.75
0.0032 141.0 141 0.8222 0.75
0.0032 142.0 142 0.8222 0.75
0.0032 143.0 143 0.8220 0.75
0.0032 144.0 144 0.8220 0.75
0.0032 145.0 145 0.8218 0.75
0.0032 146.0 146 0.8217 0.75
0.0032 147.0 147 0.8218 0.75
0.0032 148.0 148 0.8222 0.75
0.0032 149.0 149 0.8228 0.75
0.0028 150.0 150 0.8234 0.75

Framework versions

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