update model card README.md
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README.md
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---
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license: apache-2.0
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base_model: bert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: best_model-sst-2-64-42
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# best_model-sst-2-64-42
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4849
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- Accuracy: 0.8281
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 150
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 4 | 1.3914 | 0.8125 |
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| No log | 2.0 | 8 | 1.3910 | 0.8203 |
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| 0.3843 | 3.0 | 12 | 1.3922 | 0.8203 |
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| 0.3843 | 4.0 | 16 | 1.3920 | 0.8203 |
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| 0.5793 | 5.0 | 20 | 1.3923 | 0.8203 |
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| 0.5793 | 6.0 | 24 | 1.3989 | 0.8203 |
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| 0.5793 | 7.0 | 28 | 1.4029 | 0.8281 |
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| 0.3663 | 8.0 | 32 | 1.4103 | 0.8281 |
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| 0.3663 | 9.0 | 36 | 1.3999 | 0.8281 |
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| 0.2779 | 10.0 | 40 | 1.4010 | 0.8281 |
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| 0.2779 | 11.0 | 44 | 1.3978 | 0.8281 |
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| 0.2779 | 12.0 | 48 | 1.3963 | 0.8203 |
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| 0.3589 | 13.0 | 52 | 1.4087 | 0.8203 |
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| 0.3589 | 14.0 | 56 | 1.4067 | 0.8281 |
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| 0.3185 | 15.0 | 60 | 1.4148 | 0.8281 |
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| 0.3185 | 16.0 | 64 | 1.4171 | 0.8359 |
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| 0.3185 | 17.0 | 68 | 1.4140 | 0.8359 |
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| 0.1743 | 18.0 | 72 | 1.3982 | 0.8359 |
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| 0.1743 | 19.0 | 76 | 1.3650 | 0.8359 |
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| 0.1416 | 20.0 | 80 | 1.3456 | 0.8359 |
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| 0.1416 | 21.0 | 84 | 1.3210 | 0.8359 |
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| 0.1416 | 22.0 | 88 | 1.3070 | 0.8359 |
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| 0.0354 | 23.0 | 92 | 1.3015 | 0.8359 |
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| 0.0354 | 24.0 | 96 | 1.3319 | 0.8438 |
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| 0.0035 | 25.0 | 100 | 1.3656 | 0.8281 |
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| 0.0035 | 26.0 | 104 | 1.3587 | 0.8281 |
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| 0.0035 | 27.0 | 108 | 1.3243 | 0.8359 |
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| 0.0006 | 28.0 | 112 | 1.2945 | 0.8438 |
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| 0.0006 | 29.0 | 116 | 1.2898 | 0.8438 |
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| 0.0028 | 30.0 | 120 | 1.3066 | 0.8438 |
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| 0.0028 | 31.0 | 124 | 1.3055 | 0.8438 |
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| 0.0028 | 32.0 | 128 | 1.3202 | 0.8438 |
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| 0.0049 | 33.0 | 132 | 1.3351 | 0.8438 |
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| 0.0049 | 34.0 | 136 | 1.3190 | 0.8438 |
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| 0.0102 | 35.0 | 140 | 1.3141 | 0.8438 |
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| 0.0102 | 36.0 | 144 | 1.3142 | 0.8438 |
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| 0.0102 | 37.0 | 148 | 1.3647 | 0.8281 |
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| 0.0034 | 38.0 | 152 | 1.4250 | 0.8203 |
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| 0.0034 | 39.0 | 156 | 1.4708 | 0.8203 |
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| 0.0001 | 40.0 | 160 | 1.4570 | 0.8203 |
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| 0.0001 | 41.0 | 164 | 1.4446 | 0.8203 |
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| 0.0001 | 42.0 | 168 | 1.4345 | 0.8281 |
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| 0.0001 | 43.0 | 172 | 1.4272 | 0.8281 |
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| 0.0001 | 44.0 | 176 | 1.4185 | 0.8281 |
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| 0.0001 | 45.0 | 180 | 1.4048 | 0.8281 |
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| 0.0001 | 46.0 | 184 | 1.3962 | 0.8281 |
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| 0.0001 | 47.0 | 188 | 1.4924 | 0.8203 |
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| 0.0002 | 48.0 | 192 | 1.5361 | 0.8125 |
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| 0.0002 | 49.0 | 196 | 1.5831 | 0.8125 |
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| 0.0292 | 50.0 | 200 | 1.4789 | 0.8281 |
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| 0.0292 | 51.0 | 204 | 1.2642 | 0.8359 |
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| 0.0292 | 52.0 | 208 | 1.2154 | 0.8516 |
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| 0.0001 | 53.0 | 212 | 1.1895 | 0.8516 |
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| 0.0001 | 54.0 | 216 | 1.1775 | 0.8438 |
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| 0.0001 | 55.0 | 220 | 1.1730 | 0.8438 |
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| 0.0001 | 56.0 | 224 | 1.1746 | 0.8438 |
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| 0.0001 | 57.0 | 228 | 1.1782 | 0.8516 |
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| 0.0001 | 58.0 | 232 | 1.1838 | 0.8516 |
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| 0.0001 | 59.0 | 236 | 1.2456 | 0.8281 |
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| 0.025 | 60.0 | 240 | 1.3887 | 0.8281 |
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| 0.025 | 61.0 | 244 | 1.4950 | 0.8125 |
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| 0.025 | 62.0 | 248 | 1.5753 | 0.8047 |
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| 0.0001 | 63.0 | 252 | 1.6287 | 0.8047 |
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| 0.0001 | 64.0 | 256 | 1.6608 | 0.8047 |
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| 0.0001 | 65.0 | 260 | 1.6803 | 0.8047 |
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| 0.0001 | 66.0 | 264 | 1.6919 | 0.7969 |
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| 0.0001 | 67.0 | 268 | 1.5961 | 0.8047 |
|
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| 0.0001 | 68.0 | 272 | 1.4858 | 0.8125 |
|
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| 0.0001 | 69.0 | 276 | 1.4104 | 0.8281 |
|
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| 0.0001 | 70.0 | 280 | 1.3623 | 0.8281 |
|
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| 0.0001 | 71.0 | 284 | 1.3333 | 0.8359 |
|
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| 0.0001 | 72.0 | 288 | 1.3172 | 0.8359 |
|
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| 0.0 | 73.0 | 292 | 1.3107 | 0.8359 |
|
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| 0.0 | 74.0 | 296 | 1.5801 | 0.8047 |
|
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| 0.0014 | 75.0 | 300 | 1.7857 | 0.8047 |
|
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| 0.0014 | 76.0 | 304 | 1.8724 | 0.7969 |
|
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| 0.0014 | 77.0 | 308 | 1.9146 | 0.7969 |
|
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| 0.0001 | 78.0 | 312 | 1.9250 | 0.7969 |
|
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| 0.0001 | 79.0 | 316 | 1.9265 | 0.7969 |
|
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| 0.0001 | 80.0 | 320 | 1.9268 | 0.7969 |
|
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| 0.0001 | 81.0 | 324 | 1.9243 | 0.7969 |
|
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| 0.0001 | 82.0 | 328 | 1.9215 | 0.7969 |
|
135 |
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| 0.0 | 83.0 | 332 | 1.9188 | 0.7969 |
|
136 |
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| 0.0 | 84.0 | 336 | 1.9159 | 0.7969 |
|
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| 0.0 | 85.0 | 340 | 1.9137 | 0.7969 |
|
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| 0.0 | 86.0 | 344 | 1.9119 | 0.7969 |
|
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| 0.0 | 87.0 | 348 | 1.9103 | 0.7969 |
|
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| 0.0009 | 88.0 | 352 | 1.6541 | 0.8047 |
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| 0.0009 | 89.0 | 356 | 1.2749 | 0.8438 |
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| 0.0 | 90.0 | 360 | 1.2046 | 0.8438 |
|
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| 0.0 | 91.0 | 364 | 1.1909 | 0.8438 |
|
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| 0.0 | 92.0 | 368 | 1.1860 | 0.8594 |
|
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| 0.0 | 93.0 | 372 | 1.1901 | 0.8594 |
|
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| 0.0 | 94.0 | 376 | 1.1966 | 0.8516 |
|
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| 0.0001 | 95.0 | 380 | 1.2014 | 0.8516 |
|
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| 0.0001 | 96.0 | 384 | 1.2061 | 0.8438 |
|
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| 0.0001 | 97.0 | 388 | 1.2109 | 0.8438 |
|
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| 0.0 | 98.0 | 392 | 1.2170 | 0.8516 |
|
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| 0.0 | 99.0 | 396 | 1.2210 | 0.8516 |
|
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| 0.0 | 100.0 | 400 | 1.2237 | 0.8516 |
|
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| 0.0 | 101.0 | 404 | 1.2258 | 0.8516 |
|
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| 0.0 | 102.0 | 408 | 1.2276 | 0.8438 |
|
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| 0.0 | 103.0 | 412 | 1.2290 | 0.8438 |
|
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| 0.0 | 104.0 | 416 | 1.2301 | 0.8438 |
|
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| 0.0 | 105.0 | 420 | 1.2313 | 0.8438 |
|
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| 0.0 | 106.0 | 424 | 1.2324 | 0.8438 |
|
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| 0.0 | 107.0 | 428 | 1.2334 | 0.8438 |
|
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| 0.0 | 108.0 | 432 | 1.2345 | 0.8438 |
|
161 |
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| 0.0 | 109.0 | 436 | 1.2356 | 0.8438 |
|
162 |
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| 0.0 | 110.0 | 440 | 1.2366 | 0.8438 |
|
163 |
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| 0.0 | 111.0 | 444 | 1.2375 | 0.8516 |
|
164 |
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| 0.0 | 112.0 | 448 | 1.2384 | 0.8516 |
|
165 |
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| 0.0 | 113.0 | 452 | 1.2400 | 0.8516 |
|
166 |
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| 0.0 | 114.0 | 456 | 1.2415 | 0.8516 |
|
167 |
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| 0.0 | 115.0 | 460 | 1.2428 | 0.8516 |
|
168 |
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| 0.0 | 116.0 | 464 | 1.2439 | 0.8516 |
|
169 |
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| 0.0 | 117.0 | 468 | 1.2450 | 0.8516 |
|
170 |
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| 0.0 | 118.0 | 472 | 1.2459 | 0.8516 |
|
171 |
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| 0.0 | 119.0 | 476 | 1.2467 | 0.8516 |
|
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| 0.0 | 120.0 | 480 | 1.2476 | 0.8516 |
|
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| 0.0 | 121.0 | 484 | 1.2485 | 0.8516 |
|
174 |
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| 0.0 | 122.0 | 488 | 1.2495 | 0.8516 |
|
175 |
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| 0.0 | 123.0 | 492 | 1.2495 | 0.8516 |
|
176 |
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| 0.0 | 124.0 | 496 | 1.2491 | 0.8516 |
|
177 |
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| 0.0 | 125.0 | 500 | 1.2491 | 0.8516 |
|
178 |
+
| 0.0 | 126.0 | 504 | 1.2494 | 0.8516 |
|
179 |
+
| 0.0 | 127.0 | 508 | 1.2498 | 0.8516 |
|
180 |
+
| 0.0 | 128.0 | 512 | 1.2503 | 0.8516 |
|
181 |
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| 0.0 | 129.0 | 516 | 1.2509 | 0.8516 |
|
182 |
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| 0.0 | 130.0 | 520 | 1.2514 | 0.8516 |
|
183 |
+
| 0.0 | 131.0 | 524 | 1.2519 | 0.8516 |
|
184 |
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| 0.0 | 132.0 | 528 | 1.2527 | 0.8516 |
|
185 |
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| 0.0 | 133.0 | 532 | 1.2535 | 0.8516 |
|
186 |
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| 0.0 | 134.0 | 536 | 1.2542 | 0.8516 |
|
187 |
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| 0.0 | 135.0 | 540 | 1.2549 | 0.8516 |
|
188 |
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| 0.0 | 136.0 | 544 | 1.2554 | 0.8516 |
|
189 |
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| 0.0 | 137.0 | 548 | 1.3879 | 0.8359 |
|
190 |
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| 0.0001 | 138.0 | 552 | 1.6893 | 0.7969 |
|
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| 0.0001 | 139.0 | 556 | 1.8348 | 0.7969 |
|
192 |
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| 0.0 | 140.0 | 560 | 1.8942 | 0.7969 |
|
193 |
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| 0.0 | 141.0 | 564 | 1.8778 | 0.7969 |
|
194 |
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| 0.0 | 142.0 | 568 | 1.7187 | 0.8047 |
|
195 |
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| 0.0001 | 143.0 | 572 | 1.6119 | 0.8203 |
|
196 |
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| 0.0001 | 144.0 | 576 | 1.5523 | 0.8281 |
|
197 |
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| 0.0 | 145.0 | 580 | 1.5189 | 0.8281 |
|
198 |
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| 0.0 | 146.0 | 584 | 1.5008 | 0.8281 |
|
199 |
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| 0.0 | 147.0 | 588 | 1.4916 | 0.8281 |
|
200 |
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| 0.0 | 148.0 | 592 | 1.4872 | 0.8281 |
|
201 |
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| 0.0 | 149.0 | 596 | 1.4854 | 0.8281 |
|
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| 0.0 | 150.0 | 600 | 1.4849 | 0.8281 |
|
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|
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|
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### Framework versions
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- Transformers 4.32.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.4.0
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- Tokenizers 0.13.3
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