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best_model-yelp_polarity-64-42

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
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best_model-yelp_polarity-64-42 / 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-64-42
    results: []

best_model-yelp_polarity-64-42

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: 0.6069
  • Accuracy: 0.9375

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 0.7342 0.9219
No log 2.0 8 0.7290 0.9219
0.5102 3.0 12 0.7270 0.9219
0.5102 4.0 16 0.7253 0.9219
0.4089 5.0 20 0.7208 0.9219
0.4089 6.0 24 0.7191 0.9219
0.4089 7.0 28 0.7271 0.9297
0.3981 8.0 32 0.7192 0.9297
0.3981 9.0 36 0.7009 0.9219
0.1982 10.0 40 0.6963 0.9141
0.1982 11.0 44 0.6904 0.9219
0.1982 12.0 48 0.6924 0.9219
0.2128 13.0 52 0.6921 0.9297
0.2128 14.0 56 0.6866 0.9219
0.0935 15.0 60 0.6841 0.9219
0.0935 16.0 64 0.6494 0.9219
0.0935 17.0 68 0.6201 0.9219
0.0365 18.0 72 0.6122 0.9219
0.0365 19.0 76 0.6047 0.9219
0.026 20.0 80 0.5870 0.9219
0.026 21.0 84 0.5739 0.9219
0.026 22.0 88 0.5737 0.9219
0.0139 23.0 92 0.5677 0.9219
0.0139 24.0 96 0.5579 0.9219
0.0149 25.0 100 0.5468 0.9219
0.0149 26.0 104 0.5277 0.9219
0.0149 27.0 108 0.5168 0.9219
0.0085 28.0 112 0.5036 0.9141
0.0085 29.0 116 0.4960 0.9141
0.0 30.0 120 0.4941 0.9219
0.0 31.0 124 0.4956 0.9297
0.0 32.0 128 0.4987 0.9297
0.0 33.0 132 0.5018 0.9297
0.0 34.0 136 0.5053 0.9297
0.0 35.0 140 0.5081 0.9297
0.0 36.0 144 0.5107 0.9297
0.0 37.0 148 0.5125 0.9297
0.0 38.0 152 0.5135 0.9297
0.0 39.0 156 0.5146 0.9297
0.0 40.0 160 0.5157 0.9297
0.0 41.0 164 0.5168 0.9297
0.0 42.0 168 0.5182 0.9297
0.0 43.0 172 0.5197 0.9297
0.0 44.0 176 0.5209 0.9297
0.0 45.0 180 0.5224 0.9297
0.0 46.0 184 0.5240 0.9297
0.0 47.0 188 0.5257 0.9297
0.0 48.0 192 0.5272 0.9297
0.0 49.0 196 0.5286 0.9297
0.0 50.0 200 0.5300 0.9297
0.0 51.0 204 0.5313 0.9297
0.0 52.0 208 0.5329 0.9297
0.0 53.0 212 0.5343 0.9297
0.0 54.0 216 0.5355 0.9297
0.0 55.0 220 0.5369 0.9297
0.0 56.0 224 0.5382 0.9297
0.0 57.0 228 0.5395 0.9297
0.0 58.0 232 0.5407 0.9297
0.0 59.0 236 0.5419 0.9297
0.0 60.0 240 0.5431 0.9297
0.0 61.0 244 0.5444 0.9297
0.0 62.0 248 0.5455 0.9297
0.0 63.0 252 0.5466 0.9297
0.0 64.0 256 0.5478 0.9297
0.0 65.0 260 0.5489 0.9297
0.0 66.0 264 0.5501 0.9297
0.0 67.0 268 0.5513 0.9297
0.0 68.0 272 0.5524 0.9297
0.0 69.0 276 0.5535 0.9297
0.0 70.0 280 0.5548 0.9297
0.0 71.0 284 0.5559 0.9297
0.0 72.0 288 0.5570 0.9297
0.0 73.0 292 0.5581 0.9297
0.0 74.0 296 0.5592 0.9297
0.0 75.0 300 0.5601 0.9297
0.0 76.0 304 0.5610 0.9297
0.0 77.0 308 0.5620 0.9297
0.0 78.0 312 0.5630 0.9297
0.0 79.0 316 0.5640 0.9297
0.0 80.0 320 0.5648 0.9297
0.0 81.0 324 0.5658 0.9297
0.0 82.0 328 0.5667 0.9297
0.0 83.0 332 0.5675 0.9297
0.0 84.0 336 0.5684 0.9297
0.0 85.0 340 0.5693 0.9297
0.0 86.0 344 0.5701 0.9297
0.0 87.0 348 0.5710 0.9297
0.0 88.0 352 0.5719 0.9297
0.0 89.0 356 0.5728 0.9297
0.0 90.0 360 0.5736 0.9297
0.0 91.0 364 0.5745 0.9297
0.0 92.0 368 0.5754 0.9297
0.0 93.0 372 0.5762 0.9297
0.0 94.0 376 0.5771 0.9297
0.0 95.0 380 0.5779 0.9297
0.0 96.0 384 0.5788 0.9297
0.0 97.0 388 0.5796 0.9297
0.0 98.0 392 0.5804 0.9297
0.0 99.0 396 0.5812 0.9297
0.0 100.0 400 0.5820 0.9297
0.0 101.0 404 0.5828 0.9297
0.0 102.0 408 0.5836 0.9297
0.0 103.0 412 0.5843 0.9297
0.0 104.0 416 0.5851 0.9297
0.0 105.0 420 0.5859 0.9297
0.0 106.0 424 0.5866 0.9297
0.0 107.0 428 0.5874 0.9297
0.0 108.0 432 0.5881 0.9297
0.0 109.0 436 0.5889 0.9297
0.0 110.0 440 0.5896 0.9297
0.0 111.0 444 0.5902 0.9297
0.0 112.0 448 0.5910 0.9375
0.0 113.0 452 0.5916 0.9375
0.0 114.0 456 0.5924 0.9375
0.0 115.0 460 0.5931 0.9375
0.0 116.0 464 0.5938 0.9375
0.0 117.0 468 0.5945 0.9375
0.0 118.0 472 0.5952 0.9375
0.0 119.0 476 0.5958 0.9375
0.0 120.0 480 0.5964 0.9375
0.0 121.0 484 0.5971 0.9375
0.0 122.0 488 0.5978 0.9375
0.0 123.0 492 0.5985 0.9375
0.0 124.0 496 0.5991 0.9375
0.0 125.0 500 0.5997 0.9375
0.0 126.0 504 0.6004 0.9375
0.0 127.0 508 0.6009 0.9375
0.0 128.0 512 0.6015 0.9375
0.0 129.0 516 0.6020 0.9375
0.0 130.0 520 0.6025 0.9375
0.0 131.0 524 0.6029 0.9375
0.0 132.0 528 0.6034 0.9375
0.0 133.0 532 0.6038 0.9375
0.0 134.0 536 0.6042 0.9375
0.0 135.0 540 0.6045 0.9375
0.0 136.0 544 0.6048 0.9375
0.0 137.0 548 0.6051 0.9375
0.0 138.0 552 0.6054 0.9375
0.0 139.0 556 0.6056 0.9375
0.0 140.0 560 0.6058 0.9375
0.0 141.0 564 0.6061 0.9375
0.0 142.0 568 0.6062 0.9375
0.0 143.0 572 0.6064 0.9375
0.0 144.0 576 0.6065 0.9375
0.0 145.0 580 0.6066 0.9375
0.0 146.0 584 0.6067 0.9375
0.0 147.0 588 0.6068 0.9375
0.0 148.0 592 0.6068 0.9375
0.0 149.0 596 0.6069 0.9375
0.0 150.0 600 0.6069 0.9375

Framework versions

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