Hugging Face's logo

simonycl
/
best_model-sst-2-64-21

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
best_model-sst-2-64-21 / README.md
simonycl's picture
simonycl
update model card README.md
eda2b5d
preview code
|
raw
history blame contribute delete
10.6 kB
metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-sst-2-64-21
    results: []

best_model-sst-2-64-21

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

  • Loss: 1.0374
  • Accuracy: 0.8906

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 1.1068 0.8672
No log 2.0 8 1.1055 0.8672
0.5789 3.0 12 1.1002 0.8672
0.5789 4.0 16 1.0902 0.8672
0.4952 5.0 20 1.0797 0.8672
0.4952 6.0 24 1.0662 0.8672
0.4952 7.0 28 1.0461 0.8672
0.4202 8.0 32 1.0329 0.8672
0.4202 9.0 36 1.0326 0.8672
0.5159 10.0 40 1.0217 0.8672
0.5159 11.0 44 1.0053 0.8672
0.5159 12.0 48 0.9908 0.875
0.4018 13.0 52 0.9818 0.8828
0.4018 14.0 56 0.9686 0.8828
0.2452 15.0 60 0.9591 0.8828
0.2452 16.0 64 0.9489 0.8828
0.2452 17.0 68 0.9421 0.8828
0.1966 18.0 72 0.9354 0.8828
0.1966 19.0 76 0.9318 0.8906
0.1955 20.0 80 0.9353 0.8828
0.1955 21.0 84 0.9552 0.8828
0.1955 22.0 88 0.9728 0.875
0.1316 23.0 92 0.9686 0.875
0.1316 24.0 96 0.9555 0.875
0.0488 25.0 100 0.9442 0.8828
0.0488 26.0 104 0.9410 0.8828
0.0488 27.0 108 0.9413 0.8828
0.0023 28.0 112 0.9522 0.8828
0.0023 29.0 116 0.9614 0.8828
0.0019 30.0 120 0.9603 0.8828
0.0019 31.0 124 0.9474 0.8828
0.0019 32.0 128 0.9408 0.8906
0.0136 33.0 132 0.9417 0.8906
0.0136 34.0 136 0.9433 0.8906
0.0037 35.0 140 0.9412 0.8906
0.0037 36.0 144 0.9529 0.8906
0.0037 37.0 148 0.9641 0.8828
0.0003 38.0 152 0.9868 0.8828
0.0003 39.0 156 0.9985 0.875
0.0002 40.0 160 1.0006 0.875
0.0002 41.0 164 1.0009 0.875
0.0002 42.0 168 1.0038 0.875
0.0013 43.0 172 0.9982 0.8828
0.0013 44.0 176 0.9853 0.8828
0.0102 45.0 180 0.9790 0.8828
0.0102 46.0 184 0.9900 0.8828
0.0102 47.0 188 1.0004 0.8828
0.0002 48.0 192 1.0063 0.875
0.0002 49.0 196 1.0095 0.875
0.0001 50.0 200 1.0136 0.875
0.0001 51.0 204 1.0180 0.8672
0.0001 52.0 208 1.0206 0.8672
0.0001 53.0 212 1.0178 0.8672
0.0001 54.0 216 1.0157 0.8672
0.0001 55.0 220 1.0140 0.875
0.0001 56.0 224 1.0128 0.875
0.0001 57.0 228 1.0117 0.875
0.0001 58.0 232 1.0097 0.875
0.0001 59.0 236 1.0082 0.875
0.0001 60.0 240 1.0002 0.8828
0.0001 61.0 244 0.9944 0.8828
0.0001 62.0 248 0.9913 0.8906
0.0001 63.0 252 0.9897 0.8906
0.0001 64.0 256 0.9893 0.8906
0.0001 65.0 260 0.9895 0.8906
0.0001 66.0 264 0.9899 0.8906
0.0001 67.0 268 0.9905 0.8906
0.0001 68.0 272 0.9913 0.8906
0.0001 69.0 276 0.9962 0.8906
0.0001 70.0 280 1.0023 0.8828
0.0001 71.0 284 1.0079 0.8828
0.0001 72.0 288 1.0118 0.875
0.0001 73.0 292 1.0144 0.875
0.0001 74.0 296 1.0161 0.875
0.0001 75.0 300 1.0172 0.875
0.0001 76.0 304 1.0178 0.875
0.0001 77.0 308 1.0241 0.875
0.0183 78.0 312 1.0549 0.8672
0.0183 79.0 316 1.0631 0.8672
0.0001 80.0 320 1.0629 0.875
0.0001 81.0 324 1.0650 0.875
0.0001 82.0 328 1.0672 0.8594
0.0001 83.0 332 1.0686 0.8594
0.0001 84.0 336 1.0632 0.875
0.0131 85.0 340 1.0157 0.8672
0.0131 86.0 344 0.9959 0.8828
0.0131 87.0 348 0.9946 0.8906
0.0001 88.0 352 0.9933 0.8906
0.0001 89.0 356 0.9933 0.8906
0.0001 90.0 360 0.9941 0.8828
0.0001 91.0 364 0.9949 0.8828
0.0001 92.0 368 0.9954 0.8828
0.0001 93.0 372 0.9959 0.8828
0.0001 94.0 376 0.9962 0.8828
0.0001 95.0 380 0.9961 0.8828
0.0001 96.0 384 0.9963 0.8828
0.0001 97.0 388 0.9967 0.8828
0.0001 98.0 392 0.9987 0.8906
0.0001 99.0 396 1.0214 0.8828
0.0105 100.0 400 1.0346 0.875
0.0105 101.0 404 1.0406 0.875
0.0105 102.0 408 1.0435 0.875
0.0001 103.0 412 1.0444 0.875
0.0001 104.0 416 1.0446 0.875
0.0001 105.0 420 1.0447 0.875
0.0001 106.0 424 1.0448 0.875
0.0001 107.0 428 1.0453 0.8828
0.0001 108.0 432 1.0457 0.8828
0.0001 109.0 436 1.0488 0.875
0.0184 110.0 440 1.0597 0.875
0.0184 111.0 444 1.0939 0.8594
0.0184 112.0 448 1.1410 0.8438
0.0001 113.0 452 1.1659 0.8438
0.0001 114.0 456 1.1104 0.8594
0.0001 115.0 460 1.0816 0.8672
0.0001 116.0 464 1.0695 0.875
0.0001 117.0 468 1.0702 0.875
0.0 118.0 472 1.0709 0.875
0.0 119.0 476 1.0704 0.875
0.0 120.0 480 1.0693 0.875
0.0 121.0 484 1.0684 0.875
0.0 122.0 488 1.0677 0.875
0.0 123.0 492 1.0676 0.875
0.0 124.0 496 1.0676 0.875
0.0 125.0 500 1.0675 0.875
0.0 126.0 504 1.0675 0.875
0.0 127.0 508 1.0676 0.875
0.0 128.0 512 1.0687 0.875
0.0 129.0 516 1.0694 0.875
0.0 130.0 520 1.0701 0.875
0.0 131.0 524 1.0707 0.875
0.0 132.0 528 1.0661 0.875
0.0001 133.0 532 1.0391 0.8906
0.0001 134.0 536 1.0258 0.8906
0.0 135.0 540 1.0188 0.8906
0.0 136.0 544 1.0171 0.8906
0.0 137.0 548 1.0188 0.8828
0.0 138.0 552 1.0210 0.875
0.0 139.0 556 1.0223 0.875
0.0001 140.0 560 1.0202 0.8828
0.0001 141.0 564 1.0235 0.8906
0.0001 142.0 568 1.0288 0.8906
0.0 143.0 572 1.0322 0.8906
0.0 144.0 576 1.0343 0.8906
0.0 145.0 580 1.0356 0.8906
0.0 146.0 584 1.0364 0.8906
0.0 147.0 588 1.0369 0.8906
0.0 148.0 592 1.0372 0.8906
0.0 149.0 596 1.0374 0.8906
0.0 150.0 600 1.0374 0.8906

Framework versions

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