Hugging Face's logo

simonycl
/
best_model-yelp_polarity-64-87

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
Model card Files Community
1
best_model-yelp_polarity-64-87 / README.md
simonycl's picture
simonycl
update model card README.md
d8049a3
preview code
|
raw
history blame
10.6 kB
metadata
license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-yelp_polarity-64-87
    results: []

best_model-yelp_polarity-64-87

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

  • Loss: 0.6151
  • Accuracy: 0.9453

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 0.3505 0.9531
No log 2.0 8 0.3554 0.9531
0.7909 3.0 12 0.3781 0.9531
0.7909 4.0 16 0.4031 0.9531
0.5682 5.0 20 0.4409 0.9375
0.5682 6.0 24 0.5003 0.9453
0.5682 7.0 28 0.5068 0.9453
0.6452 8.0 32 0.4511 0.9453
0.6452 9.0 36 0.3963 0.9531
0.5947 10.0 40 0.3820 0.9531
0.5947 11.0 44 0.3797 0.9453
0.5947 12.0 48 0.4010 0.9453
0.6099 13.0 52 0.3783 0.9531
0.6099 14.0 56 0.3875 0.9531
0.3653 15.0 60 0.3945 0.9453
0.3653 16.0 64 0.4100 0.9453
0.3653 17.0 68 0.4161 0.9531
0.3425 18.0 72 0.4085 0.9531
0.3425 19.0 76 0.3950 0.9453
0.3247 20.0 80 0.3743 0.9531
0.3247 21.0 84 0.4230 0.9531
0.3247 22.0 88 0.4502 0.9453
0.2242 23.0 92 0.3965 0.9531
0.2242 24.0 96 0.3779 0.9453
0.1052 25.0 100 0.3940 0.9297
0.1052 26.0 104 0.4213 0.9375
0.1052 27.0 108 0.4330 0.9297
0.018 28.0 112 0.4165 0.9453
0.018 29.0 116 0.4136 0.9453
0.002 30.0 120 0.4521 0.9219
0.002 31.0 124 0.4985 0.9141
0.002 32.0 128 0.5143 0.9141
0.0002 33.0 132 0.5213 0.9141
0.0002 34.0 136 0.4808 0.9219
0.0002 35.0 140 0.4556 0.9453
0.0002 36.0 144 0.4534 0.9453
0.0002 37.0 148 0.4546 0.9453
0.0001 38.0 152 0.4599 0.9453
0.0001 39.0 156 0.4673 0.9453
0.0001 40.0 160 0.4749 0.9453
0.0001 41.0 164 0.4821 0.9453
0.0001 42.0 168 0.4891 0.9453
0.0001 43.0 172 0.4956 0.9375
0.0001 44.0 176 0.4995 0.9453
0.0001 45.0 180 0.5077 0.9375
0.0001 46.0 184 0.5162 0.9375
0.0001 47.0 188 0.5253 0.9375
0.0 48.0 192 0.5321 0.9375
0.0 49.0 196 0.5369 0.9375
0.0001 50.0 200 0.5388 0.9375
0.0001 51.0 204 0.5248 0.9453
0.0001 52.0 208 0.5274 0.9375
0.0001 53.0 212 0.5331 0.9297
0.0001 54.0 216 0.5374 0.9297
0.0 55.0 220 0.5403 0.9297
0.0 56.0 224 0.5447 0.9297
0.0 57.0 228 0.5478 0.9297
0.0 58.0 232 0.5497 0.9297
0.0 59.0 236 0.5505 0.9297
0.0 60.0 240 0.5511 0.9297
0.0 61.0 244 0.5518 0.9375
0.0 62.0 248 0.5498 0.9375
0.0 63.0 252 0.5507 0.9453
0.0 64.0 256 0.5542 0.9453
0.0 65.0 260 0.5578 0.9453
0.0 66.0 264 0.5610 0.9453
0.0 67.0 268 0.5637 0.9453
0.0 68.0 272 0.5662 0.9453
0.0 69.0 276 0.5685 0.9453
0.0 70.0 280 0.5705 0.9453
0.0 71.0 284 0.5725 0.9453
0.0 72.0 288 0.5738 0.9453
0.0 73.0 292 0.5753 0.9453
0.0 74.0 296 0.5768 0.9453
0.0 75.0 300 0.5780 0.9453
0.0 76.0 304 0.5790 0.9453
0.0 77.0 308 0.5798 0.9453
0.0 78.0 312 0.5802 0.9453
0.0 79.0 316 0.5807 0.9453
0.0 80.0 320 0.5816 0.9453
0.0 81.0 324 0.5825 0.9453
0.0 82.0 328 0.5833 0.9453
0.0 83.0 332 0.5842 0.9453
0.0 84.0 336 0.5852 0.9453
0.0 85.0 340 0.5860 0.9453
0.0 86.0 344 0.5865 0.9453
0.0 87.0 348 0.5869 0.9453
0.0 88.0 352 0.5875 0.9453
0.0 89.0 356 0.5885 0.9453
0.0 90.0 360 0.5897 0.9453
0.0 91.0 364 0.5908 0.9453
0.0 92.0 368 0.5921 0.9453
0.0 93.0 372 0.5932 0.9453
0.0 94.0 376 0.5943 0.9453
0.0 95.0 380 0.5955 0.9453
0.0 96.0 384 0.5965 0.9453
0.0 97.0 388 0.5976 0.9453
0.0 98.0 392 0.5986 0.9453
0.0 99.0 396 0.5982 0.9453
0.0 100.0 400 0.5981 0.9453
0.0 101.0 404 0.5980 0.9453
0.0 102.0 408 0.5978 0.9453
0.0 103.0 412 0.5979 0.9453
0.0 104.0 416 0.5974 0.9453
0.0 105.0 420 0.5970 0.9453
0.0 106.0 424 0.5978 0.9453
0.0 107.0 428 0.5986 0.9453
0.0 108.0 432 0.5995 0.9453
0.0 109.0 436 0.6002 0.9453
0.0 110.0 440 0.6014 0.9453
0.0 111.0 444 0.6027 0.9453
0.0 112.0 448 0.6042 0.9453
0.0 113.0 452 0.6054 0.9453
0.0 114.0 456 0.6067 0.9453
0.0 115.0 460 0.6078 0.9453
0.0 116.0 464 0.6086 0.9453
0.0 117.0 468 0.6092 0.9453
0.0 118.0 472 0.6098 0.9453
0.0 119.0 476 0.6103 0.9453
0.0 120.0 480 0.6110 0.9453
0.0 121.0 484 0.6117 0.9453
0.0 122.0 488 0.6124 0.9453
0.0 123.0 492 0.6128 0.9453
0.0 124.0 496 0.6129 0.9453
0.0 125.0 500 0.6129 0.9453
0.0 126.0 504 0.6130 0.9453
0.0 127.0 508 0.6133 0.9453
0.0 128.0 512 0.6136 0.9453
0.0 129.0 516 0.6139 0.9453
0.0 130.0 520 0.6143 0.9453
0.0 131.0 524 0.6146 0.9453
0.0 132.0 528 0.6149 0.9453
0.0 133.0 532 0.6151 0.9453
0.0 134.0 536 0.6150 0.9453
0.0 135.0 540 0.6144 0.9453
0.0 136.0 544 0.6141 0.9453
0.0 137.0 548 0.6140 0.9453
0.0 138.0 552 0.6141 0.9453
0.0 139.0 556 0.6141 0.9453
0.0 140.0 560 0.6140 0.9453
0.0 141.0 564 0.6139 0.9453
0.0 142.0 568 0.6139 0.9453
0.0 143.0 572 0.6140 0.9453
0.0 144.0 576 0.6143 0.9453
0.0 145.0 580 0.6146 0.9453
0.0 146.0 584 0.6148 0.9453
0.0 147.0 588 0.6149 0.9453
0.0 148.0 592 0.6150 0.9453
0.0 149.0 596 0.6150 0.9453
0.0 150.0 600 0.6151 0.9453

Framework versions

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