Hugging Face's logo

simonycl
/
best_model-yelp_polarity-32-100

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
Model card Files Community
1
best_model-yelp_polarity-32-100 / README.md
simonycl's picture
simonycl
update model card README.md
dbee43f
preview code
|
raw
history blame contribute delete
10.7 kB
metadata
license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-32-100
    results: []

best_model-yelp_polarity-32-100

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: 0.6759
  • 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 0.6263 0.9219
No log 2.0 4 0.6212 0.9375
No log 3.0 6 0.6183 0.9375
No log 4.0 8 0.6196 0.9375
0.3722 5.0 10 0.6224 0.9375
0.3722 6.0 12 0.6235 0.9219
0.3722 7.0 14 0.6204 0.9375
0.3722 8.0 16 0.6164 0.9375
0.3722 9.0 18 0.6145 0.9375
0.3647 10.0 20 0.6147 0.9375
0.3647 11.0 22 0.6156 0.9375
0.3647 12.0 24 0.6168 0.9375
0.3647 13.0 26 0.6201 0.9219
0.3647 14.0 28 0.6271 0.9219
0.3406 15.0 30 0.6346 0.9219
0.3406 16.0 32 0.6356 0.9219
0.3406 17.0 34 0.6223 0.9219
0.3406 18.0 36 0.6258 0.9219
0.3406 19.0 38 0.6385 0.9219
0.2674 20.0 40 0.6640 0.9219
0.2674 21.0 42 0.6955 0.9219
0.2674 22.0 44 0.7032 0.9219
0.2674 23.0 46 0.7005 0.9219
0.2674 24.0 48 0.6918 0.9219
0.1287 25.0 50 0.6602 0.9219
0.1287 26.0 52 0.6141 0.9219
0.1287 27.0 54 0.5937 0.8906
0.1287 28.0 56 0.5987 0.9062
0.1287 29.0 58 0.6046 0.8906
0.0668 30.0 60 0.5944 0.8906
0.0668 31.0 62 0.5954 0.8906
0.0668 32.0 64 0.5907 0.9062
0.0668 33.0 66 0.5787 0.9062
0.0668 34.0 68 0.5778 0.9062
0.0463 35.0 70 0.5876 0.9062
0.0463 36.0 72 0.6117 0.9219
0.0463 37.0 74 0.6575 0.9219
0.0463 38.0 76 0.6886 0.9219
0.0463 39.0 78 0.7052 0.9219
0.0233 40.0 80 0.7125 0.9219
0.0233 41.0 82 0.7227 0.9219
0.0233 42.0 84 0.7260 0.9219
0.0233 43.0 86 0.7399 0.9219
0.0233 44.0 88 0.7464 0.9219
0.0012 45.0 90 0.7493 0.9219
0.0012 46.0 92 0.7494 0.9219
0.0012 47.0 94 0.7478 0.9219
0.0012 48.0 96 0.7453 0.9219
0.0012 49.0 98 0.7420 0.9219
0.0001 50.0 100 0.7381 0.9219
0.0001 51.0 102 0.7340 0.9219
0.0001 52.0 104 0.7297 0.9219
0.0001 53.0 106 0.7253 0.9219
0.0001 54.0 108 0.7210 0.9219
0.0 55.0 110 0.7168 0.9219
0.0 56.0 112 0.7128 0.9219
0.0 57.0 114 0.7090 0.9219
0.0 58.0 116 0.7054 0.9219
0.0 59.0 118 0.7020 0.9219
0.0 60.0 120 0.6989 0.9219
0.0 61.0 122 0.6961 0.9219
0.0 62.0 124 0.6934 0.9219
0.0 63.0 126 0.6909 0.9219
0.0 64.0 128 0.6887 0.9219
0.0 65.0 130 0.6865 0.9219
0.0 66.0 132 0.6846 0.9219
0.0 67.0 134 0.6827 0.9219
0.0 68.0 136 0.6811 0.9219
0.0 69.0 138 0.6795 0.9219
0.0 70.0 140 0.6780 0.9219
0.0 71.0 142 0.6766 0.9219
0.0 72.0 144 0.6754 0.9219
0.0 73.0 146 0.6742 0.9219
0.0 74.0 148 0.6731 0.9219
0.0 75.0 150 0.6721 0.9219
0.0 76.0 152 0.6711 0.9219
0.0 77.0 154 0.6702 0.9219
0.0 78.0 156 0.6693 0.9219
0.0 79.0 158 0.6685 0.9219
0.0 80.0 160 0.6677 0.9219
0.0 81.0 162 0.6670 0.9219
0.0 82.0 164 0.6664 0.9219
0.0 83.0 166 0.6658 0.9219
0.0 84.0 168 0.6653 0.9219
0.0 85.0 170 0.6648 0.9219
0.0 86.0 172 0.6644 0.9219
0.0 87.0 174 0.6641 0.9062
0.0 88.0 176 0.6638 0.9062
0.0 89.0 178 0.6635 0.9062
0.0 90.0 180 0.6633 0.9062
0.0 91.0 182 0.6631 0.9062
0.0 92.0 184 0.6629 0.9062
0.0 93.0 186 0.6628 0.9062
0.0 94.0 188 0.6628 0.9062
0.0 95.0 190 0.6627 0.9062
0.0 96.0 192 0.6627 0.9062
0.0 97.0 194 0.6627 0.9062
0.0 98.0 196 0.6627 0.9062
0.0 99.0 198 0.6628 0.9062
0.0 100.0 200 0.6628 0.9062
0.0 101.0 202 0.6629 0.9062
0.0 102.0 204 0.6629 0.9062
0.0 103.0 206 0.6630 0.9062
0.0 104.0 208 0.6632 0.9062
0.0 105.0 210 0.6633 0.9062
0.0 106.0 212 0.6634 0.9219
0.0 107.0 214 0.6636 0.9219
0.0 108.0 216 0.6638 0.9219
0.0 109.0 218 0.6640 0.9219
0.0 110.0 220 0.6642 0.9219
0.0 111.0 222 0.6644 0.9219
0.0 112.0 224 0.6646 0.9219
0.0 113.0 226 0.6648 0.9219
0.0 114.0 228 0.6650 0.9219
0.0 115.0 230 0.6653 0.9219
0.0 116.0 232 0.6656 0.9219
0.0 117.0 234 0.6659 0.9219
0.0 118.0 236 0.6661 0.9219
0.0 119.0 238 0.6664 0.9219
0.0 120.0 240 0.6667 0.9219
0.0 121.0 242 0.6670 0.9219
0.0 122.0 244 0.6672 0.9219
0.0 123.0 246 0.6675 0.9219
0.0 124.0 248 0.6679 0.9219
0.0 125.0 250 0.6681 0.9219
0.0 126.0 252 0.6684 0.9219
0.0 127.0 254 0.6687 0.9219
0.0 128.0 256 0.6691 0.9219
0.0 129.0 258 0.6694 0.9219
0.0 130.0 260 0.6698 0.9219
0.0 131.0 262 0.6702 0.9219
0.0 132.0 264 0.6706 0.9219
0.0 133.0 266 0.6709 0.9219
0.0 134.0 268 0.6712 0.9219
0.0 135.0 270 0.6716 0.9219
0.0 136.0 272 0.6719 0.9219
0.0 137.0 274 0.6722 0.9219
0.0 138.0 276 0.6725 0.9219
0.0 139.0 278 0.6727 0.9219
0.0 140.0 280 0.6730 0.9219
0.0 141.0 282 0.6732 0.9219
0.0 142.0 284 0.6734 0.9219
0.0 143.0 286 0.6737 0.9219
0.0 144.0 288 0.6740 0.9219
0.0 145.0 290 0.6744 0.9219
0.0 146.0 292 0.6747 0.9219
0.0 147.0 294 0.6750 0.9219
0.0 148.0 296 0.6753 0.9219
0.0 149.0 298 0.6757 0.9219
0.0 150.0 300 0.6759 0.9219

Framework versions

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