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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-2-16-21
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-2-16-21
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: 0.4916
- Accuracy: 0.7812
## 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 | 0.6220 | 0.7188 |
| No log | 2.0 | 2 | 0.6220 | 0.7188 |
| No log | 3.0 | 3 | 0.6219 | 0.7188 |
| No log | 4.0 | 4 | 0.6218 | 0.7188 |
| No log | 5.0 | 5 | 0.6217 | 0.7188 |
| No log | 6.0 | 6 | 0.6216 | 0.7188 |
| No log | 7.0 | 7 | 0.6215 | 0.7188 |
| No log | 8.0 | 8 | 0.6214 | 0.7188 |
| No log | 9.0 | 9 | 0.6213 | 0.7188 |
| 0.6205 | 10.0 | 10 | 0.6211 | 0.7188 |
| 0.6205 | 11.0 | 11 | 0.6210 | 0.7188 |
| 0.6205 | 12.0 | 12 | 0.6208 | 0.7188 |
| 0.6205 | 13.0 | 13 | 0.6206 | 0.7188 |
| 0.6205 | 14.0 | 14 | 0.6204 | 0.7188 |
| 0.6205 | 15.0 | 15 | 0.6202 | 0.7188 |
| 0.6205 | 16.0 | 16 | 0.6200 | 0.7188 |
| 0.6205 | 17.0 | 17 | 0.6198 | 0.7188 |
| 0.6205 | 18.0 | 18 | 0.6196 | 0.7188 |
| 0.6205 | 19.0 | 19 | 0.6194 | 0.7188 |
| 0.6217 | 20.0 | 20 | 0.6192 | 0.6875 |
| 0.6217 | 21.0 | 21 | 0.6190 | 0.6875 |
| 0.6217 | 22.0 | 22 | 0.6189 | 0.6875 |
| 0.6217 | 23.0 | 23 | 0.6187 | 0.6875 |
| 0.6217 | 24.0 | 24 | 0.6186 | 0.6875 |
| 0.6217 | 25.0 | 25 | 0.6185 | 0.6875 |
| 0.6217 | 26.0 | 26 | 0.6184 | 0.6875 |
| 0.6217 | 27.0 | 27 | 0.6182 | 0.6875 |
| 0.6217 | 28.0 | 28 | 0.6181 | 0.6875 |
| 0.6217 | 29.0 | 29 | 0.6180 | 0.6875 |
| 0.6001 | 30.0 | 30 | 0.6179 | 0.6875 |
| 0.6001 | 31.0 | 31 | 0.6178 | 0.6875 |
| 0.6001 | 32.0 | 32 | 0.6178 | 0.6875 |
| 0.6001 | 33.0 | 33 | 0.6177 | 0.6875 |
| 0.6001 | 34.0 | 34 | 0.6177 | 0.6875 |
| 0.6001 | 35.0 | 35 | 0.6177 | 0.6875 |
| 0.6001 | 36.0 | 36 | 0.6177 | 0.6875 |
| 0.6001 | 37.0 | 37 | 0.6178 | 0.6875 |
| 0.6001 | 38.0 | 38 | 0.6178 | 0.6875 |
| 0.6001 | 39.0 | 39 | 0.6179 | 0.6562 |
| 0.5564 | 40.0 | 40 | 0.6180 | 0.6562 |
| 0.5564 | 41.0 | 41 | 0.6181 | 0.6562 |
| 0.5564 | 42.0 | 42 | 0.6181 | 0.6562 |
| 0.5564 | 43.0 | 43 | 0.6180 | 0.6562 |
| 0.5564 | 44.0 | 44 | 0.6179 | 0.6562 |
| 0.5564 | 45.0 | 45 | 0.6177 | 0.6562 |
| 0.5564 | 46.0 | 46 | 0.6174 | 0.6562 |
| 0.5564 | 47.0 | 47 | 0.6171 | 0.6562 |
| 0.5564 | 48.0 | 48 | 0.6171 | 0.6562 |
| 0.5564 | 49.0 | 49 | 0.6170 | 0.6562 |
| 0.5364 | 50.0 | 50 | 0.6172 | 0.6562 |
| 0.5364 | 51.0 | 51 | 0.6172 | 0.625 |
| 0.5364 | 52.0 | 52 | 0.6172 | 0.625 |
| 0.5364 | 53.0 | 53 | 0.6170 | 0.625 |
| 0.5364 | 54.0 | 54 | 0.6165 | 0.625 |
| 0.5364 | 55.0 | 55 | 0.6161 | 0.625 |
| 0.5364 | 56.0 | 56 | 0.6155 | 0.625 |
| 0.5364 | 57.0 | 57 | 0.6149 | 0.625 |
| 0.5364 | 58.0 | 58 | 0.6142 | 0.625 |
| 0.5364 | 59.0 | 59 | 0.6137 | 0.625 |
| 0.489 | 60.0 | 60 | 0.6132 | 0.625 |
| 0.489 | 61.0 | 61 | 0.6126 | 0.625 |
| 0.489 | 62.0 | 62 | 0.6121 | 0.625 |
| 0.489 | 63.0 | 63 | 0.6116 | 0.625 |
| 0.489 | 64.0 | 64 | 0.6111 | 0.5938 |
| 0.489 | 65.0 | 65 | 0.6107 | 0.5938 |
| 0.489 | 66.0 | 66 | 0.6103 | 0.625 |
| 0.489 | 67.0 | 67 | 0.6098 | 0.625 |
| 0.489 | 68.0 | 68 | 0.6093 | 0.625 |
| 0.489 | 69.0 | 69 | 0.6088 | 0.625 |
| 0.4517 | 70.0 | 70 | 0.6086 | 0.625 |
| 0.4517 | 71.0 | 71 | 0.6080 | 0.625 |
| 0.4517 | 72.0 | 72 | 0.6068 | 0.625 |
| 0.4517 | 73.0 | 73 | 0.6052 | 0.625 |
| 0.4517 | 74.0 | 74 | 0.6035 | 0.625 |
| 0.4517 | 75.0 | 75 | 0.6014 | 0.625 |
| 0.4517 | 76.0 | 76 | 0.5993 | 0.625 |
| 0.4517 | 77.0 | 77 | 0.5974 | 0.625 |
| 0.4517 | 78.0 | 78 | 0.5951 | 0.6562 |
| 0.4517 | 79.0 | 79 | 0.5932 | 0.6875 |
| 0.4066 | 80.0 | 80 | 0.5912 | 0.6875 |
| 0.4066 | 81.0 | 81 | 0.5895 | 0.6875 |
| 0.4066 | 82.0 | 82 | 0.5880 | 0.6875 |
| 0.4066 | 83.0 | 83 | 0.5868 | 0.6875 |
| 0.4066 | 84.0 | 84 | 0.5856 | 0.7188 |
| 0.4066 | 85.0 | 85 | 0.5843 | 0.7188 |
| 0.4066 | 86.0 | 86 | 0.5829 | 0.7188 |
| 0.4066 | 87.0 | 87 | 0.5816 | 0.7188 |
| 0.4066 | 88.0 | 88 | 0.5803 | 0.7188 |
| 0.4066 | 89.0 | 89 | 0.5790 | 0.7188 |
| 0.3548 | 90.0 | 90 | 0.5778 | 0.7188 |
| 0.3548 | 91.0 | 91 | 0.5766 | 0.75 |
| 0.3548 | 92.0 | 92 | 0.5754 | 0.75 |
| 0.3548 | 93.0 | 93 | 0.5743 | 0.75 |
| 0.3548 | 94.0 | 94 | 0.5732 | 0.75 |
| 0.3548 | 95.0 | 95 | 0.5719 | 0.75 |
| 0.3548 | 96.0 | 96 | 0.5706 | 0.75 |
| 0.3548 | 97.0 | 97 | 0.5693 | 0.75 |
| 0.3548 | 98.0 | 98 | 0.5680 | 0.75 |
| 0.3548 | 99.0 | 99 | 0.5669 | 0.75 |
| 0.3182 | 100.0 | 100 | 0.5659 | 0.75 |
| 0.3182 | 101.0 | 101 | 0.5648 | 0.7812 |
| 0.3182 | 102.0 | 102 | 0.5636 | 0.7812 |
| 0.3182 | 103.0 | 103 | 0.5624 | 0.7812 |
| 0.3182 | 104.0 | 104 | 0.5612 | 0.7812 |
| 0.3182 | 105.0 | 105 | 0.5599 | 0.7812 |
| 0.3182 | 106.0 | 106 | 0.5584 | 0.7812 |
| 0.3182 | 107.0 | 107 | 0.5567 | 0.8125 |
| 0.3182 | 108.0 | 108 | 0.5548 | 0.8125 |
| 0.3182 | 109.0 | 109 | 0.5530 | 0.8125 |
| 0.2758 | 110.0 | 110 | 0.5513 | 0.8125 |
| 0.2758 | 111.0 | 111 | 0.5498 | 0.8125 |
| 0.2758 | 112.0 | 112 | 0.5483 | 0.8125 |
| 0.2758 | 113.0 | 113 | 0.5468 | 0.7812 |
| 0.2758 | 114.0 | 114 | 0.5452 | 0.7812 |
| 0.2758 | 115.0 | 115 | 0.5438 | 0.7812 |
| 0.2758 | 116.0 | 116 | 0.5429 | 0.7812 |
| 0.2758 | 117.0 | 117 | 0.5420 | 0.75 |
| 0.2758 | 118.0 | 118 | 0.5411 | 0.75 |
| 0.2758 | 119.0 | 119 | 0.5400 | 0.7188 |
| 0.2399 | 120.0 | 120 | 0.5390 | 0.7188 |
| 0.2399 | 121.0 | 121 | 0.5375 | 0.7188 |
| 0.2399 | 122.0 | 122 | 0.5356 | 0.75 |
| 0.2399 | 123.0 | 123 | 0.5340 | 0.75 |
| 0.2399 | 124.0 | 124 | 0.5322 | 0.75 |
| 0.2399 | 125.0 | 125 | 0.5303 | 0.7812 |
| 0.2399 | 126.0 | 126 | 0.5281 | 0.75 |
| 0.2399 | 127.0 | 127 | 0.5259 | 0.7812 |
| 0.2399 | 128.0 | 128 | 0.5240 | 0.7812 |
| 0.2399 | 129.0 | 129 | 0.5215 | 0.7812 |
| 0.2062 | 130.0 | 130 | 0.5191 | 0.75 |
| 0.2062 | 131.0 | 131 | 0.5167 | 0.75 |
| 0.2062 | 132.0 | 132 | 0.5143 | 0.75 |
| 0.2062 | 133.0 | 133 | 0.5121 | 0.75 |
| 0.2062 | 134.0 | 134 | 0.5102 | 0.75 |
| 0.2062 | 135.0 | 135 | 0.5085 | 0.7812 |
| 0.2062 | 136.0 | 136 | 0.5071 | 0.7812 |
| 0.2062 | 137.0 | 137 | 0.5059 | 0.7812 |
| 0.2062 | 138.0 | 138 | 0.5048 | 0.7812 |
| 0.2062 | 139.0 | 139 | 0.5035 | 0.7812 |
| 0.1688 | 140.0 | 140 | 0.5023 | 0.7812 |
| 0.1688 | 141.0 | 141 | 0.5011 | 0.7812 |
| 0.1688 | 142.0 | 142 | 0.5001 | 0.7812 |
| 0.1688 | 143.0 | 143 | 0.4991 | 0.7812 |
| 0.1688 | 144.0 | 144 | 0.4980 | 0.7812 |
| 0.1688 | 145.0 | 145 | 0.4970 | 0.7812 |
| 0.1688 | 146.0 | 146 | 0.4960 | 0.7812 |
| 0.1688 | 147.0 | 147 | 0.4950 | 0.7812 |
| 0.1688 | 148.0 | 148 | 0.4938 | 0.7812 |
| 0.1688 | 149.0 | 149 | 0.4926 | 0.7812 |
| 0.1364 | 150.0 | 150 | 0.4916 | 0.7812 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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