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best_model-yelp_polarity-64-13

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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best_model-yelp_polarity-64-13 / 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-64-13
    results: []

best_model-yelp_polarity-64-13

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: 0.9118
  • Accuracy: 0.9062

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 4 0.9825 0.8828
No log 2.0 8 0.9391 0.8906
0.0791 3.0 12 0.8979 0.8984
0.0791 4.0 16 0.8416 0.875
0.0238 5.0 20 0.8260 0.8906
0.0238 6.0 24 0.8079 0.8984
0.0238 7.0 28 0.7782 0.8906
0.0015 8.0 32 0.7635 0.8984
0.0015 9.0 36 0.7694 0.9062
0.0001 10.0 40 0.7757 0.9062
0.0001 11.0 44 0.7786 0.9141
0.0001 12.0 48 0.7749 0.9141
0.0 13.0 52 0.7730 0.9141
0.0 14.0 56 0.7692 0.9141
0.0 15.0 60 0.7662 0.9141
0.0 16.0 64 0.7640 0.9141
0.0 17.0 68 0.7616 0.9141
0.0 18.0 72 0.7600 0.9141
0.0 19.0 76 0.7608 0.9141
0.0 20.0 80 0.7625 0.9141
0.0 21.0 84 0.7641 0.9141
0.0 22.0 88 0.7656 0.9141
0.0 23.0 92 0.7670 0.9141
0.0 24.0 96 0.7692 0.9141
0.0 25.0 100 0.7709 0.9141
0.0 26.0 104 0.7737 0.9141
0.0 27.0 108 0.7763 0.9141
0.0 28.0 112 0.7774 0.9141
0.0 29.0 116 0.7802 0.9141
0.0 30.0 120 0.7819 0.9141
0.0 31.0 124 0.7846 0.9141
0.0 32.0 128 0.7864 0.9141
0.0 33.0 132 0.7891 0.9141
0.0 34.0 136 0.7923 0.9141
0.0 35.0 140 0.7953 0.9141
0.0 36.0 144 0.7967 0.9141
0.0 37.0 148 0.7973 0.9141
0.0 38.0 152 0.7987 0.9141
0.0 39.0 156 0.8002 0.9141
0.0 40.0 160 0.8022 0.9141
0.0 41.0 164 0.8030 0.9141
0.0 42.0 168 0.8043 0.9141
0.0 43.0 172 0.8048 0.9141
0.0 44.0 176 0.8057 0.9141
0.0 45.0 180 0.8068 0.9141
0.0 46.0 184 0.8080 0.9141
0.0 47.0 188 0.8104 0.9141
0.0 48.0 192 0.8121 0.9141
0.0 49.0 196 0.8122 0.9141
0.0 50.0 200 0.8133 0.9141
0.0 51.0 204 0.8146 0.9141
0.0 52.0 208 0.8154 0.9141
0.0 53.0 212 0.8160 0.9141
0.0 54.0 216 0.8182 0.9141
0.0 55.0 220 0.8204 0.9141
0.0 56.0 224 0.8226 0.9141
0.0 57.0 228 0.8228 0.9141
0.0 58.0 232 0.8241 0.9141
0.0 59.0 236 0.8263 0.9141
0.0 60.0 240 0.8284 0.9062
0.0 61.0 244 0.8287 0.9062
0.0 62.0 248 0.8300 0.9062
0.0 63.0 252 0.8317 0.9062
0.0 64.0 256 0.8327 0.9062
0.0 65.0 260 0.8342 0.9062
0.0 66.0 264 0.8353 0.9062
0.0 67.0 268 0.8369 0.9062
0.0 68.0 272 0.8378 0.9062
0.0 69.0 276 0.8386 0.9062
0.0 70.0 280 0.8394 0.9062
0.0 71.0 284 0.8403 0.9062
0.0 72.0 288 0.8413 0.9062
0.0 73.0 292 0.8414 0.9062
0.0 74.0 296 0.8430 0.9062
0.0 75.0 300 0.8439 0.9062
0.0 76.0 304 0.8452 0.9062
0.0 77.0 308 0.8469 0.9062
0.0 78.0 312 0.8484 0.9062
0.0 79.0 316 0.8499 0.9062
0.0 80.0 320 0.8517 0.9062
0.0 81.0 324 0.8533 0.9062
0.0 82.0 328 0.8538 0.9062
0.0 83.0 332 0.8549 0.9062
0.0 84.0 336 0.8565 0.9062
0.0 85.0 340 0.8575 0.9062
0.0 86.0 344 0.8585 0.9062
0.0 87.0 348 0.8596 0.9062
0.0 88.0 352 0.8609 0.9062
0.0 89.0 356 0.8623 0.9062
0.0 90.0 360 0.8641 0.9062
0.0 91.0 364 0.8653 0.9062
0.0 92.0 368 0.8664 0.9062
0.0 93.0 372 0.8674 0.9062
0.0 94.0 376 0.8695 0.9062
0.0 95.0 380 0.8711 0.9062
0.0 96.0 384 0.8715 0.9062
0.0 97.0 388 0.8713 0.9062
0.0 98.0 392 0.8725 0.9062
0.0 99.0 396 0.8725 0.9062
0.0 100.0 400 0.8730 0.9062
0.0 101.0 404 0.8730 0.9062
0.0 102.0 408 0.8738 0.9062
0.0 103.0 412 0.8750 0.9062
0.0 104.0 416 0.8756 0.9062
0.0 105.0 420 0.8757 0.9062
0.0 106.0 424 0.8772 0.9062
0.0 107.0 428 0.8785 0.9062
0.0 108.0 432 0.8795 0.9062
0.0 109.0 436 0.8806 0.9062
0.0 110.0 440 0.8815 0.9062
0.0 111.0 444 0.8826 0.9062
0.0 112.0 448 0.8837 0.9062
0.0 113.0 452 0.8846 0.9062
0.0 114.0 456 0.8859 0.9062
0.0 115.0 460 0.8877 0.9062
0.0 116.0 464 0.8891 0.9062
0.0 117.0 468 0.8913 0.9062
0.0 118.0 472 0.8926 0.9062
0.0 119.0 476 0.8940 0.9062
0.0 120.0 480 0.8959 0.9062
0.0 121.0 484 0.8978 0.9062
0.0 122.0 488 0.8987 0.9062
0.0 123.0 492 0.8999 0.9062
0.0 124.0 496 0.8998 0.9062
0.0 125.0 500 0.9010 0.9062
0.0 126.0 504 0.9019 0.9062
0.0 127.0 508 0.9031 0.9062
0.0 128.0 512 0.9036 0.9062
0.0 129.0 516 0.9039 0.9062
0.0 130.0 520 0.9043 0.9062
0.0 131.0 524 0.9043 0.9062
0.0 132.0 528 0.9052 0.9062
0.0 133.0 532 0.9052 0.9062
0.0 134.0 536 0.9060 0.9062
0.0 135.0 540 0.9071 0.9062
0.0 136.0 544 0.9078 0.9062
0.0 137.0 548 0.9085 0.9062
0.0 138.0 552 0.9087 0.9062
0.0 139.0 556 0.9094 0.9062
0.0 140.0 560 0.9097 0.9062
0.0 141.0 564 0.9101 0.9062
0.0 142.0 568 0.9105 0.9062
0.0 143.0 572 0.9108 0.9062
0.0 144.0 576 0.9110 0.9062
0.0 145.0 580 0.9112 0.9062
0.0 146.0 584 0.9115 0.9062
0.0 147.0 588 0.9116 0.9062
0.0 148.0 592 0.9117 0.9062
0.0 149.0 596 0.9118 0.9062
0.0 150.0 600 0.9118 0.9062

Framework versions

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