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best_model-yelp_polarity-32-21

Text Classification
Transformers
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-32-21
    results: []

best_model-yelp_polarity-32-21

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: 0.8940
  • Accuracy: 0.875

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 2 0.8845 0.875
No log 2.0 4 0.8817 0.875
No log 3.0 6 0.8770 0.875
No log 4.0 8 0.8735 0.875
0.4208 5.0 10 0.8676 0.875
0.4208 6.0 12 0.8661 0.875
0.4208 7.0 14 0.8671 0.875
0.4208 8.0 16 0.8603 0.875
0.4208 9.0 18 0.8539 0.875
0.3008 10.0 20 0.8486 0.875
0.3008 11.0 22 0.8322 0.875
0.3008 12.0 24 0.8044 0.875
0.3008 13.0 26 0.7829 0.875
0.3008 14.0 28 0.7727 0.875
0.1225 15.0 30 0.7704 0.875
0.1225 16.0 32 0.7792 0.8594
0.1225 17.0 34 0.7959 0.8594
0.1225 18.0 36 0.8441 0.8594
0.1225 19.0 38 0.8519 0.8594
0.0141 20.0 40 0.8216 0.8594
0.0141 21.0 42 0.7810 0.875
0.0141 22.0 44 0.7611 0.875
0.0141 23.0 46 0.7566 0.875
0.0141 24.0 48 0.7634 0.875
0.0011 25.0 50 0.7747 0.875
0.0011 26.0 52 0.7894 0.8594
0.0011 27.0 54 0.8063 0.8594
0.0011 28.0 56 0.8136 0.8594
0.0011 29.0 58 0.8142 0.8594
0.0003 30.0 60 0.8096 0.8594
0.0003 31.0 62 0.8001 0.8594
0.0003 32.0 64 0.7901 0.8594
0.0003 33.0 66 0.7819 0.875
0.0003 34.0 68 0.7763 0.875
0.0002 35.0 70 0.7729 0.875
0.0002 36.0 72 0.7707 0.875
0.0002 37.0 74 0.7693 0.875
0.0002 38.0 76 0.7684 0.875
0.0002 39.0 78 0.7684 0.875
0.0002 40.0 80 0.7686 0.875
0.0002 41.0 82 0.7692 0.875
0.0002 42.0 84 0.7701 0.875
0.0002 43.0 86 0.7712 0.875
0.0002 44.0 88 0.7726 0.875
0.0002 45.0 90 0.7741 0.875
0.0002 46.0 92 0.7758 0.875
0.0002 47.0 94 0.7778 0.875
0.0002 48.0 96 0.7796 0.875
0.0002 49.0 98 0.7815 0.875
0.0001 50.0 100 0.7835 0.875
0.0001 51.0 102 0.7855 0.875
0.0001 52.0 104 0.7872 0.875
0.0001 53.0 106 0.7888 0.875
0.0001 54.0 108 0.7905 0.875
0.0001 55.0 110 0.7922 0.875
0.0001 56.0 112 0.7938 0.875
0.0001 57.0 114 0.7954 0.875
0.0001 58.0 116 0.7969 0.875
0.0001 59.0 118 0.7982 0.875
0.0001 60.0 120 0.7995 0.875
0.0001 61.0 122 0.8007 0.875
0.0001 62.0 124 0.8020 0.875
0.0001 63.0 126 0.8031 0.875
0.0001 64.0 128 0.8041 0.875
0.0001 65.0 130 0.8052 0.875
0.0001 66.0 132 0.8063 0.875
0.0001 67.0 134 0.8073 0.875
0.0001 68.0 136 0.8084 0.875
0.0001 69.0 138 0.8095 0.875
0.0001 70.0 140 0.8104 0.875
0.0001 71.0 142 0.8115 0.875
0.0001 72.0 144 0.8125 0.875
0.0001 73.0 146 0.8135 0.875
0.0001 74.0 148 0.8143 0.875
0.0001 75.0 150 0.8151 0.875
0.0001 76.0 152 0.8159 0.875
0.0001 77.0 154 0.8167 0.875
0.0001 78.0 156 0.8176 0.875
0.0001 79.0 158 0.8187 0.875
0.0001 80.0 160 0.8198 0.875
0.0001 81.0 162 0.8210 0.875
0.0001 82.0 164 0.8222 0.875
0.0001 83.0 166 0.8232 0.875
0.0001 84.0 168 0.8243 0.875
0.0001 85.0 170 0.8254 0.875
0.0001 86.0 172 0.8266 0.875
0.0001 87.0 174 0.8278 0.875
0.0001 88.0 176 0.8290 0.875
0.0001 89.0 178 0.8302 0.875
0.0001 90.0 180 0.8314 0.875
0.0001 91.0 182 0.8326 0.875
0.0001 92.0 184 0.8337 0.875
0.0001 93.0 186 0.8347 0.875
0.0001 94.0 188 0.8358 0.875
0.0001 95.0 190 0.8369 0.875
0.0001 96.0 192 0.8379 0.875
0.0001 97.0 194 0.8390 0.875
0.0001 98.0 196 0.8401 0.875
0.0001 99.0 198 0.8411 0.875
0.0001 100.0 200 0.8421 0.875
0.0001 101.0 202 0.8431 0.875
0.0001 102.0 204 0.8442 0.875
0.0001 103.0 206 0.8454 0.875
0.0001 104.0 208 0.8464 0.875
0.0001 105.0 210 0.8475 0.875
0.0001 106.0 212 0.8486 0.875
0.0001 107.0 214 0.8498 0.875
0.0001 108.0 216 0.8510 0.875
0.0001 109.0 218 0.8520 0.875
0.0001 110.0 220 0.8532 0.875
0.0001 111.0 222 0.8544 0.875
0.0001 112.0 224 0.8556 0.875
0.0001 113.0 226 0.8568 0.875
0.0001 114.0 228 0.8580 0.875
0.0 115.0 230 0.8591 0.875
0.0 116.0 232 0.8601 0.875
0.0 117.0 234 0.8612 0.875
0.0 118.0 236 0.8623 0.875
0.0 119.0 238 0.8633 0.875
0.0 120.0 240 0.8643 0.875
0.0 121.0 242 0.8652 0.875
0.0 122.0 244 0.8662 0.875
0.0 123.0 246 0.8671 0.875
0.0 124.0 248 0.8680 0.875
0.0 125.0 250 0.8689 0.875
0.0 126.0 252 0.8699 0.875
0.0 127.0 254 0.8708 0.875
0.0 128.0 256 0.8717 0.875
0.0 129.0 258 0.8727 0.875
0.0 130.0 260 0.8736 0.875
0.0 131.0 262 0.8746 0.875
0.0 132.0 264 0.8755 0.875
0.0 133.0 266 0.8764 0.875
0.0 134.0 268 0.8774 0.875
0.0 135.0 270 0.8784 0.875
0.0 136.0 272 0.8794 0.875
0.0 137.0 274 0.8803 0.875
0.0 138.0 276 0.8814 0.875
0.0 139.0 278 0.8825 0.875
0.0 140.0 280 0.8835 0.875
0.0 141.0 282 0.8846 0.875
0.0 142.0 284 0.8857 0.875
0.0 143.0 286 0.8869 0.875
0.0 144.0 288 0.8880 0.875
0.0 145.0 290 0.8890 0.875
0.0 146.0 292 0.8900 0.875
0.0 147.0 294 0.8911 0.875
0.0 148.0 296 0.8921 0.875
0.0 149.0 298 0.8931 0.875
0.0 150.0 300 0.8940 0.875

Framework versions

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