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best_model-yelp_polarity-32-13

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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best_model-yelp_polarity-32-13 / README.md
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metadata
license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-32-13
    results: []

best_model-yelp_polarity-32-13

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

  • Loss: 0.7343
  • Accuracy: 0.9219

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 0.5150 0.9375
No log 2.0 4 0.5183 0.9375
No log 3.0 6 0.5239 0.9375
No log 4.0 8 0.5297 0.9375
0.121 5.0 10 0.5354 0.9375
0.121 6.0 12 0.5416 0.9375
0.121 7.0 14 0.5505 0.9375
0.121 8.0 16 0.5631 0.9219
0.121 9.0 18 0.5919 0.9219
0.0647 10.0 20 0.6157 0.9219
0.0647 11.0 22 0.6462 0.9062
0.0647 12.0 24 0.6650 0.9062
0.0647 13.0 26 0.6774 0.9062
0.0647 14.0 28 0.6785 0.9062
0.0493 15.0 30 0.6712 0.9062
0.0493 16.0 32 0.6561 0.9062
0.0493 17.0 34 0.6397 0.9219
0.0493 18.0 36 0.6254 0.9219
0.0493 19.0 38 0.6044 0.9219
0.0344 20.0 40 0.5844 0.9219
0.0344 21.0 42 0.5757 0.9219
0.0344 22.0 44 0.5695 0.9219
0.0344 23.0 46 0.5683 0.9219
0.0344 24.0 48 0.5848 0.9219
0.0002 25.0 50 0.6016 0.9219
0.0002 26.0 52 0.6158 0.9219
0.0002 27.0 54 0.6269 0.9219
0.0002 28.0 56 0.6424 0.9219
0.0002 29.0 58 0.6560 0.9219
0.0039 30.0 60 0.6640 0.9219
0.0039 31.0 62 0.6670 0.9219
0.0039 32.0 64 0.6696 0.9219
0.0039 33.0 66 0.6720 0.9219
0.0039 34.0 68 0.6731 0.9219
0.0002 35.0 70 0.6740 0.9219
0.0002 36.0 72 0.6748 0.9219
0.0002 37.0 74 0.6735 0.9219
0.0002 38.0 76 0.6727 0.9219
0.0002 39.0 78 0.6710 0.9219
0.0001 40.0 80 0.6682 0.9219
0.0001 41.0 82 0.6650 0.9219
0.0001 42.0 84 0.6767 0.9219
0.0001 43.0 86 0.6856 0.9219
0.0001 44.0 88 0.6906 0.9219
0.0001 45.0 90 0.6949 0.9219
0.0001 46.0 92 0.6931 0.9219
0.0001 47.0 94 0.6904 0.9219
0.0001 48.0 96 0.6855 0.9219
0.0001 49.0 98 0.6793 0.9219
0.0002 50.0 100 0.6721 0.9219
0.0002 51.0 102 0.6642 0.9219
0.0002 52.0 104 0.6566 0.9219
0.0002 53.0 106 0.6494 0.9219
0.0002 54.0 108 0.6429 0.9219
0.0001 55.0 110 0.6377 0.9219
0.0001 56.0 112 0.6401 0.9219
0.0001 57.0 114 0.6488 0.9219
0.0001 58.0 116 0.6571 0.9219
0.0001 59.0 118 0.6641 0.9219
0.0001 60.0 120 0.6696 0.9219
0.0001 61.0 122 0.6740 0.9219
0.0001 62.0 124 0.6776 0.9219
0.0001 63.0 126 0.6806 0.9219
0.0001 64.0 128 0.6831 0.9219
0.0001 65.0 130 0.6851 0.9219
0.0001 66.0 132 0.6871 0.9219
0.0001 67.0 134 0.6893 0.9219
0.0001 68.0 136 0.6912 0.9219
0.0001 69.0 138 0.6925 0.9219
0.0001 70.0 140 0.6936 0.9219
0.0001 71.0 142 0.6946 0.9219
0.0001 72.0 144 0.6956 0.9219
0.0001 73.0 146 0.6963 0.9219
0.0001 74.0 148 0.6969 0.9219
0.0001 75.0 150 0.6972 0.9219
0.0001 76.0 152 0.6977 0.9219
0.0001 77.0 154 0.6982 0.9219
0.0001 78.0 156 0.6984 0.9219
0.0001 79.0 158 0.6989 0.9219
0.0001 80.0 160 0.6996 0.9219
0.0001 81.0 162 0.7006 0.9219
0.0001 82.0 164 0.7011 0.9219
0.0001 83.0 166 0.7016 0.9219
0.0001 84.0 168 0.7024 0.9219
0.0001 85.0 170 0.7030 0.9219
0.0001 86.0 172 0.7038 0.9219
0.0001 87.0 174 0.7051 0.9219
0.0001 88.0 176 0.7061 0.9219
0.0001 89.0 178 0.7072 0.9219
0.0001 90.0 180 0.7082 0.9219
0.0001 91.0 182 0.7091 0.9219
0.0001 92.0 184 0.7099 0.9219
0.0001 93.0 186 0.7107 0.9219
0.0001 94.0 188 0.7116 0.9219
0.0001 95.0 190 0.7126 0.9219
0.0001 96.0 192 0.7136 0.9219
0.0001 97.0 194 0.7146 0.9219
0.0001 98.0 196 0.7156 0.9219
0.0001 99.0 198 0.7165 0.9219
0.0001 100.0 200 0.7172 0.9219
0.0001 101.0 202 0.7172 0.9219
0.0001 102.0 204 0.7174 0.9219
0.0001 103.0 206 0.7178 0.9219
0.0001 104.0 208 0.7188 0.9219
0.0001 105.0 210 0.7195 0.9219
0.0001 106.0 212 0.7203 0.9219
0.0001 107.0 214 0.7212 0.9219
0.0001 108.0 216 0.7220 0.9219
0.0001 109.0 218 0.7230 0.9219
0.0001 110.0 220 0.7247 0.9219
0.0001 111.0 222 0.7264 0.9219
0.0001 112.0 224 0.7280 0.9219
0.0001 113.0 226 0.7294 0.9219
0.0001 114.0 228 0.7313 0.9219
0.0001 115.0 230 0.7328 0.9219
0.0001 116.0 232 0.7343 0.9219
0.0001 117.0 234 0.7357 0.9219
0.0001 118.0 236 0.7369 0.9219
0.0001 119.0 238 0.7378 0.9219
0.0001 120.0 240 0.7387 0.9219
0.0001 121.0 242 0.7394 0.9219
0.0001 122.0 244 0.7401 0.9219
0.0001 123.0 246 0.7409 0.9219
0.0001 124.0 248 0.7418 0.9219
0.0 125.0 250 0.7427 0.9219
0.0 126.0 252 0.7438 0.9219
0.0 127.0 254 0.7451 0.9219
0.0 128.0 256 0.7463 0.9219
0.0 129.0 258 0.7474 0.9219
0.0 130.0 260 0.7486 0.9219
0.0 131.0 262 0.7500 0.9219
0.0 132.0 264 0.7514 0.9219
0.0 133.0 266 0.7528 0.9219
0.0 134.0 268 0.8507 0.8906
0.0001 135.0 270 1.0733 0.8906
0.0001 136.0 272 1.2689 0.8594
0.0001 137.0 274 0.9691 0.8906
0.0001 138.0 276 0.7454 0.9062
0.0001 139.0 278 0.7415 0.9219
0.0136 140.0 280 0.7437 0.9219
0.0136 141.0 282 0.7095 0.9219
0.0136 142.0 284 0.6249 0.9219
0.0136 143.0 286 0.5231 0.9375
0.0136 144.0 288 0.4934 0.9531
0.0 145.0 290 0.4934 0.9531
0.0 146.0 292 0.6506 0.9219
0.0 147.0 294 0.7018 0.9219
0.0 148.0 296 0.6696 0.9219
0.0 149.0 298 0.7124 0.9219
0.022 150.0 300 0.7343 0.9219

Framework versions

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