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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-2-16-87
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-2-16-87
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: 0.6392
- Accuracy: 0.875
## 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 | 1 | 0.5816 | 0.8438 |
| No log | 2.0 | 2 | 0.5813 | 0.8438 |
| No log | 3.0 | 3 | 0.5807 | 0.8438 |
| No log | 4.0 | 4 | 0.5798 | 0.8438 |
| No log | 5.0 | 5 | 0.5786 | 0.8438 |
| No log | 6.0 | 6 | 0.5770 | 0.8438 |
| No log | 7.0 | 7 | 0.5750 | 0.8438 |
| No log | 8.0 | 8 | 0.5726 | 0.8438 |
| No log | 9.0 | 9 | 0.5701 | 0.8438 |
| 0.4546 | 10.0 | 10 | 0.5672 | 0.8438 |
| 0.4546 | 11.0 | 11 | 0.5641 | 0.8438 |
| 0.4546 | 12.0 | 12 | 0.5614 | 0.8438 |
| 0.4546 | 13.0 | 13 | 0.5586 | 0.8438 |
| 0.4546 | 14.0 | 14 | 0.5560 | 0.8438 |
| 0.4546 | 15.0 | 15 | 0.5530 | 0.8438 |
| 0.4546 | 16.0 | 16 | 0.5501 | 0.8438 |
| 0.4546 | 17.0 | 17 | 0.5470 | 0.8438 |
| 0.4546 | 18.0 | 18 | 0.5438 | 0.8438 |
| 0.4546 | 19.0 | 19 | 0.5407 | 0.8438 |
| 0.4413 | 20.0 | 20 | 0.5369 | 0.8438 |
| 0.4413 | 21.0 | 21 | 0.5325 | 0.8438 |
| 0.4413 | 22.0 | 22 | 0.5280 | 0.8438 |
| 0.4413 | 23.0 | 23 | 0.5230 | 0.8438 |
| 0.4413 | 24.0 | 24 | 0.5180 | 0.8438 |
| 0.4413 | 25.0 | 25 | 0.5132 | 0.8438 |
| 0.4413 | 26.0 | 26 | 0.5088 | 0.8438 |
| 0.4413 | 27.0 | 27 | 0.5049 | 0.8438 |
| 0.4413 | 28.0 | 28 | 0.5014 | 0.8438 |
| 0.4413 | 29.0 | 29 | 0.4985 | 0.8438 |
| 0.3899 | 30.0 | 30 | 0.4964 | 0.8438 |
| 0.3899 | 31.0 | 31 | 0.4951 | 0.8438 |
| 0.3899 | 32.0 | 32 | 0.4937 | 0.8438 |
| 0.3899 | 33.0 | 33 | 0.4919 | 0.8438 |
| 0.3899 | 34.0 | 34 | 0.4902 | 0.8438 |
| 0.3899 | 35.0 | 35 | 0.4884 | 0.8438 |
| 0.3899 | 36.0 | 36 | 0.4870 | 0.8438 |
| 0.3899 | 37.0 | 37 | 0.4854 | 0.8438 |
| 0.3899 | 38.0 | 38 | 0.4844 | 0.8438 |
| 0.3899 | 39.0 | 39 | 0.4832 | 0.875 |
| 0.3672 | 40.0 | 40 | 0.4821 | 0.875 |
| 0.3672 | 41.0 | 41 | 0.4817 | 0.875 |
| 0.3672 | 42.0 | 42 | 0.4817 | 0.875 |
| 0.3672 | 43.0 | 43 | 0.4820 | 0.875 |
| 0.3672 | 44.0 | 44 | 0.4830 | 0.875 |
| 0.3672 | 45.0 | 45 | 0.4838 | 0.875 |
| 0.3672 | 46.0 | 46 | 0.4848 | 0.875 |
| 0.3672 | 47.0 | 47 | 0.4855 | 0.875 |
| 0.3672 | 48.0 | 48 | 0.4854 | 0.875 |
| 0.3672 | 49.0 | 49 | 0.4860 | 0.875 |
| 0.2765 | 50.0 | 50 | 0.4872 | 0.875 |
| 0.2765 | 51.0 | 51 | 0.4878 | 0.875 |
| 0.2765 | 52.0 | 52 | 0.4892 | 0.875 |
| 0.2765 | 53.0 | 53 | 0.4913 | 0.875 |
| 0.2765 | 54.0 | 54 | 0.4942 | 0.8438 |
| 0.2765 | 55.0 | 55 | 0.4977 | 0.8438 |
| 0.2765 | 56.0 | 56 | 0.5017 | 0.8438 |
| 0.2765 | 57.0 | 57 | 0.5074 | 0.8438 |
| 0.2765 | 58.0 | 58 | 0.5148 | 0.8438 |
| 0.2765 | 59.0 | 59 | 0.5211 | 0.8438 |
| 0.2106 | 60.0 | 60 | 0.5286 | 0.8438 |
| 0.2106 | 61.0 | 61 | 0.5361 | 0.8438 |
| 0.2106 | 62.0 | 62 | 0.5429 | 0.8438 |
| 0.2106 | 63.0 | 63 | 0.5497 | 0.8438 |
| 0.2106 | 64.0 | 64 | 0.5551 | 0.8438 |
| 0.2106 | 65.0 | 65 | 0.5569 | 0.8438 |
| 0.2106 | 66.0 | 66 | 0.5556 | 0.8438 |
| 0.2106 | 67.0 | 67 | 0.5522 | 0.8438 |
| 0.2106 | 68.0 | 68 | 0.5465 | 0.8438 |
| 0.2106 | 69.0 | 69 | 0.5400 | 0.8438 |
| 0.1587 | 70.0 | 70 | 0.5359 | 0.8438 |
| 0.1587 | 71.0 | 71 | 0.5311 | 0.8438 |
| 0.1587 | 72.0 | 72 | 0.5252 | 0.8438 |
| 0.1587 | 73.0 | 73 | 0.5217 | 0.8438 |
| 0.1587 | 74.0 | 74 | 0.5192 | 0.8438 |
| 0.1587 | 75.0 | 75 | 0.5158 | 0.8438 |
| 0.1587 | 76.0 | 76 | 0.5128 | 0.8438 |
| 0.1587 | 77.0 | 77 | 0.5113 | 0.8438 |
| 0.1587 | 78.0 | 78 | 0.5105 | 0.8438 |
| 0.1587 | 79.0 | 79 | 0.5091 | 0.8438 |
| 0.122 | 80.0 | 80 | 0.5090 | 0.8438 |
| 0.122 | 81.0 | 81 | 0.5100 | 0.8438 |
| 0.122 | 82.0 | 82 | 0.5126 | 0.8438 |
| 0.122 | 83.0 | 83 | 0.5167 | 0.8438 |
| 0.122 | 84.0 | 84 | 0.5215 | 0.8438 |
| 0.122 | 85.0 | 85 | 0.5274 | 0.8438 |
| 0.122 | 86.0 | 86 | 0.5351 | 0.8438 |
| 0.122 | 87.0 | 87 | 0.5439 | 0.8438 |
| 0.122 | 88.0 | 88 | 0.5547 | 0.8438 |
| 0.122 | 89.0 | 89 | 0.5658 | 0.8438 |
| 0.0738 | 90.0 | 90 | 0.5778 | 0.8438 |
| 0.0738 | 91.0 | 91 | 0.5872 | 0.8438 |
| 0.0738 | 92.0 | 92 | 0.5963 | 0.8438 |
| 0.0738 | 93.0 | 93 | 0.6027 | 0.8438 |
| 0.0738 | 94.0 | 94 | 0.6059 | 0.8438 |
| 0.0738 | 95.0 | 95 | 0.6070 | 0.8438 |
| 0.0738 | 96.0 | 96 | 0.6052 | 0.8438 |
| 0.0738 | 97.0 | 97 | 0.6020 | 0.8438 |
| 0.0738 | 98.0 | 98 | 0.5950 | 0.8438 |
| 0.0738 | 99.0 | 99 | 0.5870 | 0.8438 |
| 0.0328 | 100.0 | 100 | 0.5788 | 0.8438 |
| 0.0328 | 101.0 | 101 | 0.5706 | 0.8438 |
| 0.0328 | 102.0 | 102 | 0.5638 | 0.8438 |
| 0.0328 | 103.0 | 103 | 0.5578 | 0.8438 |
| 0.0328 | 104.0 | 104 | 0.5530 | 0.8438 |
| 0.0328 | 105.0 | 105 | 0.5491 | 0.875 |
| 0.0328 | 106.0 | 106 | 0.5465 | 0.875 |
| 0.0328 | 107.0 | 107 | 0.5457 | 0.875 |
| 0.0328 | 108.0 | 108 | 0.5456 | 0.875 |
| 0.0328 | 109.0 | 109 | 0.5462 | 0.875 |
| 0.0221 | 110.0 | 110 | 0.5473 | 0.875 |
| 0.0221 | 111.0 | 111 | 0.5486 | 0.875 |
| 0.0221 | 112.0 | 112 | 0.5500 | 0.875 |
| 0.0221 | 113.0 | 113 | 0.5521 | 0.875 |
| 0.0221 | 114.0 | 114 | 0.5543 | 0.875 |
| 0.0221 | 115.0 | 115 | 0.5564 | 0.875 |
| 0.0221 | 116.0 | 116 | 0.5589 | 0.875 |
| 0.0221 | 117.0 | 117 | 0.5613 | 0.875 |
| 0.0221 | 118.0 | 118 | 0.5637 | 0.875 |
| 0.0221 | 119.0 | 119 | 0.5660 | 0.875 |
| 0.017 | 120.0 | 120 | 0.5682 | 0.875 |
| 0.017 | 121.0 | 121 | 0.5704 | 0.875 |
| 0.017 | 122.0 | 122 | 0.5727 | 0.875 |
| 0.017 | 123.0 | 123 | 0.5748 | 0.875 |
| 0.017 | 124.0 | 124 | 0.5772 | 0.875 |
| 0.017 | 125.0 | 125 | 0.5796 | 0.875 |
| 0.017 | 126.0 | 126 | 0.5820 | 0.875 |
| 0.017 | 127.0 | 127 | 0.5847 | 0.875 |
| 0.017 | 128.0 | 128 | 0.5874 | 0.875 |
| 0.017 | 129.0 | 129 | 0.5900 | 0.875 |
| 0.0129 | 130.0 | 130 | 0.5926 | 0.875 |
| 0.0129 | 131.0 | 131 | 0.5951 | 0.875 |
| 0.0129 | 132.0 | 132 | 0.5976 | 0.875 |
| 0.0129 | 133.0 | 133 | 0.6001 | 0.875 |
| 0.0129 | 134.0 | 134 | 0.6027 | 0.875 |
| 0.0129 | 135.0 | 135 | 0.6051 | 0.875 |
| 0.0129 | 136.0 | 136 | 0.6076 | 0.875 |
| 0.0129 | 137.0 | 137 | 0.6099 | 0.875 |
| 0.0129 | 138.0 | 138 | 0.6123 | 0.875 |
| 0.0129 | 139.0 | 139 | 0.6146 | 0.875 |
| 0.0103 | 140.0 | 140 | 0.6169 | 0.875 |
| 0.0103 | 141.0 | 141 | 0.6192 | 0.875 |
| 0.0103 | 142.0 | 142 | 0.6216 | 0.875 |
| 0.0103 | 143.0 | 143 | 0.6239 | 0.875 |
| 0.0103 | 144.0 | 144 | 0.6261 | 0.875 |
| 0.0103 | 145.0 | 145 | 0.6284 | 0.875 |
| 0.0103 | 146.0 | 146 | 0.6306 | 0.875 |
| 0.0103 | 147.0 | 147 | 0.6328 | 0.875 |
| 0.0103 | 148.0 | 148 | 0.6350 | 0.875 |
| 0.0103 | 149.0 | 149 | 0.6371 | 0.875 |
| 0.0084 | 150.0 | 150 | 0.6392 | 0.875 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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