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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-sst-2-32-100
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# best_model-sst-2-32-100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5168
- Accuracy: 0.9219
## 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 | 2 | 0.8101 | 0.9062 |
| No log | 2.0 | 4 | 0.8102 | 0.9062 |
| No log | 3.0 | 6 | 0.8102 | 0.9062 |
| No log | 4.0 | 8 | 0.8100 | 0.9062 |
| 0.6019 | 5.0 | 10 | 0.8098 | 0.9062 |
| 0.6019 | 6.0 | 12 | 0.8095 | 0.9062 |
| 0.6019 | 7.0 | 14 | 0.8090 | 0.9062 |
| 0.6019 | 8.0 | 16 | 0.8085 | 0.9062 |
| 0.6019 | 9.0 | 18 | 0.8079 | 0.9062 |
| 0.6181 | 10.0 | 20 | 0.8073 | 0.9062 |
| 0.6181 | 11.0 | 22 | 0.8066 | 0.9062 |
| 0.6181 | 12.0 | 24 | 0.8061 | 0.9062 |
| 0.6181 | 13.0 | 26 | 0.8055 | 0.9062 |
| 0.6181 | 14.0 | 28 | 0.8048 | 0.9062 |
| 0.5045 | 15.0 | 30 | 0.8037 | 0.9062 |
| 0.5045 | 16.0 | 32 | 0.8020 | 0.9062 |
| 0.5045 | 17.0 | 34 | 0.8003 | 0.9062 |
| 0.5045 | 18.0 | 36 | 0.7978 | 0.9062 |
| 0.5045 | 19.0 | 38 | 0.7955 | 0.9062 |
| 0.4784 | 20.0 | 40 | 0.7928 | 0.9062 |
| 0.4784 | 21.0 | 42 | 0.7902 | 0.9062 |
| 0.4784 | 22.0 | 44 | 0.7868 | 0.9062 |
| 0.4784 | 23.0 | 46 | 0.7824 | 0.9062 |
| 0.4784 | 24.0 | 48 | 0.7764 | 0.9062 |
| 0.3582 | 25.0 | 50 | 0.7695 | 0.9062 |
| 0.3582 | 26.0 | 52 | 0.7628 | 0.9062 |
| 0.3582 | 27.0 | 54 | 0.7548 | 0.9062 |
| 0.3582 | 28.0 | 56 | 0.7473 | 0.9062 |
| 0.3582 | 29.0 | 58 | 0.7388 | 0.9062 |
| 0.3152 | 30.0 | 60 | 0.7286 | 0.9062 |
| 0.3152 | 31.0 | 62 | 0.7145 | 0.9062 |
| 0.3152 | 32.0 | 64 | 0.7007 | 0.9062 |
| 0.3152 | 33.0 | 66 | 0.6860 | 0.9062 |
| 0.3152 | 34.0 | 68 | 0.6662 | 0.9062 |
| 0.2403 | 35.0 | 70 | 0.6377 | 0.9062 |
| 0.2403 | 36.0 | 72 | 0.5941 | 0.9062 |
| 0.2403 | 37.0 | 74 | 0.5458 | 0.8906 |
| 0.2403 | 38.0 | 76 | 0.4985 | 0.8906 |
| 0.2403 | 39.0 | 78 | 0.4676 | 0.9219 |
| 0.1021 | 40.0 | 80 | 0.4598 | 0.9219 |
| 0.1021 | 41.0 | 82 | 0.4572 | 0.9375 |
| 0.1021 | 42.0 | 84 | 0.4521 | 0.9375 |
| 0.1021 | 43.0 | 86 | 0.4493 | 0.9375 |
| 0.1021 | 44.0 | 88 | 0.4420 | 0.9375 |
| 0.016 | 45.0 | 90 | 0.4264 | 0.9375 |
| 0.016 | 46.0 | 92 | 0.4104 | 0.9375 |
| 0.016 | 47.0 | 94 | 0.4008 | 0.9375 |
| 0.016 | 48.0 | 96 | 0.4056 | 0.9062 |
| 0.016 | 49.0 | 98 | 0.4256 | 0.9219 |
| 0.0016 | 50.0 | 100 | 0.4450 | 0.9062 |
| 0.0016 | 51.0 | 102 | 0.4667 | 0.9062 |
| 0.0016 | 52.0 | 104 | 0.4946 | 0.9062 |
| 0.0016 | 53.0 | 106 | 0.5189 | 0.9062 |
| 0.0016 | 54.0 | 108 | 0.5347 | 0.9062 |
| 0.0008 | 55.0 | 110 | 0.5434 | 0.9062 |
| 0.0008 | 56.0 | 112 | 0.5500 | 0.9062 |
| 0.0008 | 57.0 | 114 | 0.5545 | 0.9062 |
| 0.0008 | 58.0 | 116 | 0.5557 | 0.9062 |
| 0.0008 | 59.0 | 118 | 0.5535 | 0.9062 |
| 0.0005 | 60.0 | 120 | 0.5492 | 0.9062 |
| 0.0005 | 61.0 | 122 | 0.5389 | 0.9062 |
| 0.0005 | 62.0 | 124 | 0.5249 | 0.9062 |
| 0.0005 | 63.0 | 126 | 0.5044 | 0.9062 |
| 0.0005 | 64.0 | 128 | 0.4804 | 0.9062 |
| 0.0008 | 65.0 | 130 | 0.4611 | 0.9219 |
| 0.0008 | 66.0 | 132 | 0.4474 | 0.9375 |
| 0.0008 | 67.0 | 134 | 0.4373 | 0.9375 |
| 0.0008 | 68.0 | 136 | 0.4299 | 0.9375 |
| 0.0008 | 69.0 | 138 | 0.4246 | 0.9219 |
| 0.0003 | 70.0 | 140 | 0.4213 | 0.9219 |
| 0.0003 | 71.0 | 142 | 0.4191 | 0.9219 |
| 0.0003 | 72.0 | 144 | 0.4177 | 0.9219 |
| 0.0003 | 73.0 | 146 | 0.4283 | 0.9219 |
| 0.0003 | 74.0 | 148 | 0.4393 | 0.9375 |
| 0.0011 | 75.0 | 150 | 0.4489 | 0.9375 |
| 0.0011 | 76.0 | 152 | 0.4577 | 0.9375 |
| 0.0011 | 77.0 | 154 | 0.4659 | 0.9375 |
| 0.0011 | 78.0 | 156 | 0.4734 | 0.9219 |
| 0.0011 | 79.0 | 158 | 0.4803 | 0.9219 |
| 0.0003 | 80.0 | 160 | 0.4866 | 0.9219 |
| 0.0003 | 81.0 | 162 | 0.4924 | 0.9062 |
| 0.0003 | 82.0 | 164 | 0.4845 | 0.9219 |
| 0.0003 | 83.0 | 166 | 0.4663 | 0.9375 |
| 0.0003 | 84.0 | 168 | 0.4532 | 0.9375 |
| 0.0072 | 85.0 | 170 | 0.4429 | 0.9375 |
| 0.0072 | 86.0 | 172 | 0.4352 | 0.9375 |
| 0.0072 | 87.0 | 174 | 0.4297 | 0.9375 |
| 0.0072 | 88.0 | 176 | 0.4255 | 0.9219 |
| 0.0072 | 89.0 | 178 | 0.4223 | 0.9219 |
| 0.0002 | 90.0 | 180 | 0.4201 | 0.9219 |
| 0.0002 | 91.0 | 182 | 0.4184 | 0.9219 |
| 0.0002 | 92.0 | 184 | 0.4171 | 0.9219 |
| 0.0002 | 93.0 | 186 | 0.4163 | 0.9219 |
| 0.0002 | 94.0 | 188 | 0.4231 | 0.9219 |
| 0.0002 | 95.0 | 190 | 0.4306 | 0.9375 |
| 0.0002 | 96.0 | 192 | 0.4377 | 0.9375 |
| 0.0002 | 97.0 | 194 | 0.4440 | 0.9375 |
| 0.0002 | 98.0 | 196 | 0.4494 | 0.9375 |
| 0.0002 | 99.0 | 198 | 0.4542 | 0.9375 |
| 0.0002 | 100.0 | 200 | 0.4582 | 0.9375 |
| 0.0002 | 101.0 | 202 | 0.4617 | 0.9375 |
| 0.0002 | 102.0 | 204 | 0.4646 | 0.9375 |
| 0.0002 | 103.0 | 206 | 0.4676 | 0.9375 |
| 0.0002 | 104.0 | 208 | 0.4705 | 0.9375 |
| 0.0002 | 105.0 | 210 | 0.4729 | 0.9375 |
| 0.0002 | 106.0 | 212 | 0.4749 | 0.9375 |
| 0.0002 | 107.0 | 214 | 0.4769 | 0.9375 |
| 0.0002 | 108.0 | 216 | 0.4788 | 0.9375 |
| 0.0002 | 109.0 | 218 | 0.4803 | 0.9375 |
| 0.0002 | 110.0 | 220 | 0.4810 | 0.9375 |
| 0.0002 | 111.0 | 222 | 0.4817 | 0.9375 |
| 0.0002 | 112.0 | 224 | 0.4825 | 0.9375 |
| 0.0002 | 113.0 | 226 | 0.4837 | 0.9375 |
| 0.0002 | 114.0 | 228 | 0.4849 | 0.9375 |
| 0.0002 | 115.0 | 230 | 0.4857 | 0.9219 |
| 0.0002 | 116.0 | 232 | 0.4679 | 0.9375 |
| 0.0002 | 117.0 | 234 | 0.4374 | 0.9375 |
| 0.0002 | 118.0 | 236 | 0.4225 | 0.9375 |
| 0.0002 | 119.0 | 238 | 0.4275 | 0.9375 |
| 0.0004 | 120.0 | 240 | 0.4352 | 0.9375 |
| 0.0004 | 121.0 | 242 | 0.4423 | 0.9375 |
| 0.0004 | 122.0 | 244 | 0.4481 | 0.9375 |
| 0.0004 | 123.0 | 246 | 0.4509 | 0.9375 |
| 0.0004 | 124.0 | 248 | 0.4527 | 0.9375 |
| 0.0002 | 125.0 | 250 | 0.4528 | 0.9375 |
| 0.0002 | 126.0 | 252 | 0.4530 | 0.9375 |
| 0.0002 | 127.0 | 254 | 0.4531 | 0.9375 |
| 0.0002 | 128.0 | 256 | 0.4531 | 0.9375 |
| 0.0002 | 129.0 | 258 | 0.4530 | 0.9375 |
| 0.0014 | 130.0 | 260 | 0.4188 | 0.9375 |
| 0.0014 | 131.0 | 262 | 0.4099 | 0.9531 |
| 0.0014 | 132.0 | 264 | 0.4306 | 0.9219 |
| 0.0014 | 133.0 | 266 | 0.4583 | 0.9219 |
| 0.0014 | 134.0 | 268 | 0.4801 | 0.9219 |
| 0.0001 | 135.0 | 270 | 0.4951 | 0.9219 |
| 0.0001 | 136.0 | 272 | 0.5056 | 0.9219 |
| 0.0001 | 137.0 | 274 | 0.5134 | 0.9062 |
| 0.0001 | 138.0 | 276 | 0.5179 | 0.9062 |
| 0.0001 | 139.0 | 278 | 0.5215 | 0.9062 |
| 0.0001 | 140.0 | 280 | 0.5243 | 0.9062 |
| 0.0001 | 141.0 | 282 | 0.5255 | 0.9062 |
| 0.0001 | 142.0 | 284 | 0.5258 | 0.9062 |
| 0.0001 | 143.0 | 286 | 0.5261 | 0.9062 |
| 0.0001 | 144.0 | 288 | 0.5262 | 0.9062 |
| 0.0001 | 145.0 | 290 | 0.5261 | 0.9062 |
| 0.0001 | 146.0 | 292 | 0.5236 | 0.9062 |
| 0.0001 | 147.0 | 294 | 0.5214 | 0.9062 |
| 0.0001 | 148.0 | 296 | 0.5194 | 0.9219 |
| 0.0001 | 149.0 | 298 | 0.5177 | 0.9219 |
| 0.0001 | 150.0 | 300 | 0.5168 | 0.9219 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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