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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-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-yelp_polarity-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: 0.2887
- Accuracy: 0.8438
## 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 | 0.3259 | 0.875 |
| No log | 2.0 | 2 | 0.3259 | 0.875 |
| No log | 3.0 | 3 | 0.3257 | 0.875 |
| No log | 4.0 | 4 | 0.3256 | 0.875 |
| No log | 5.0 | 5 | 0.3254 | 0.875 |
| No log | 6.0 | 6 | 0.3251 | 0.875 |
| No log | 7.0 | 7 | 0.3247 | 0.875 |
| No log | 8.0 | 8 | 0.3243 | 0.875 |
| No log | 9.0 | 9 | 0.3238 | 0.875 |
| 0.2717 | 10.0 | 10 | 0.3233 | 0.875 |
| 0.2717 | 11.0 | 11 | 0.3227 | 0.875 |
| 0.2717 | 12.0 | 12 | 0.3220 | 0.875 |
| 0.2717 | 13.0 | 13 | 0.3212 | 0.875 |
| 0.2717 | 14.0 | 14 | 0.3204 | 0.875 |
| 0.2717 | 15.0 | 15 | 0.3195 | 0.875 |
| 0.2717 | 16.0 | 16 | 0.3185 | 0.875 |
| 0.2717 | 17.0 | 17 | 0.3174 | 0.875 |
| 0.2717 | 18.0 | 18 | 0.3161 | 0.875 |
| 0.2717 | 19.0 | 19 | 0.3148 | 0.875 |
| 0.2339 | 20.0 | 20 | 0.3134 | 0.875 |
| 0.2339 | 21.0 | 21 | 0.3119 | 0.875 |
| 0.2339 | 22.0 | 22 | 0.3103 | 0.875 |
| 0.2339 | 23.0 | 23 | 0.3087 | 0.875 |
| 0.2339 | 24.0 | 24 | 0.3072 | 0.875 |
| 0.2339 | 25.0 | 25 | 0.3056 | 0.875 |
| 0.2339 | 26.0 | 26 | 0.3038 | 0.875 |
| 0.2339 | 27.0 | 27 | 0.3021 | 0.875 |
| 0.2339 | 28.0 | 28 | 0.3003 | 0.875 |
| 0.2339 | 29.0 | 29 | 0.2985 | 0.875 |
| 0.1912 | 30.0 | 30 | 0.2967 | 0.875 |
| 0.1912 | 31.0 | 31 | 0.2948 | 0.875 |
| 0.1912 | 32.0 | 32 | 0.2931 | 0.875 |
| 0.1912 | 33.0 | 33 | 0.2913 | 0.875 |
| 0.1912 | 34.0 | 34 | 0.2895 | 0.875 |
| 0.1912 | 35.0 | 35 | 0.2876 | 0.875 |
| 0.1912 | 36.0 | 36 | 0.2858 | 0.875 |
| 0.1912 | 37.0 | 37 | 0.2840 | 0.875 |
| 0.1912 | 38.0 | 38 | 0.2822 | 0.875 |
| 0.1912 | 39.0 | 39 | 0.2803 | 0.875 |
| 0.115 | 40.0 | 40 | 0.2785 | 0.875 |
| 0.115 | 41.0 | 41 | 0.2767 | 0.9062 |
| 0.115 | 42.0 | 42 | 0.2750 | 0.9062 |
| 0.115 | 43.0 | 43 | 0.2732 | 0.9062 |
| 0.115 | 44.0 | 44 | 0.2713 | 0.9062 |
| 0.115 | 45.0 | 45 | 0.2694 | 0.9062 |
| 0.115 | 46.0 | 46 | 0.2676 | 0.9062 |
| 0.115 | 47.0 | 47 | 0.2658 | 0.9062 |
| 0.115 | 48.0 | 48 | 0.2640 | 0.9062 |
| 0.115 | 49.0 | 49 | 0.2625 | 0.9062 |
| 0.0852 | 50.0 | 50 | 0.2612 | 0.9062 |
| 0.0852 | 51.0 | 51 | 0.2604 | 0.875 |
| 0.0852 | 52.0 | 52 | 0.2601 | 0.875 |
| 0.0852 | 53.0 | 53 | 0.2607 | 0.8438 |
| 0.0852 | 54.0 | 54 | 0.2623 | 0.8438 |
| 0.0852 | 55.0 | 55 | 0.2655 | 0.8438 |
| 0.0852 | 56.0 | 56 | 0.2683 | 0.8438 |
| 0.0852 | 57.0 | 57 | 0.2702 | 0.8438 |
| 0.0852 | 58.0 | 58 | 0.2712 | 0.875 |
| 0.0852 | 59.0 | 59 | 0.2724 | 0.875 |
| 0.0595 | 60.0 | 60 | 0.2739 | 0.875 |
| 0.0595 | 61.0 | 61 | 0.2749 | 0.875 |
| 0.0595 | 62.0 | 62 | 0.2746 | 0.8438 |
| 0.0595 | 63.0 | 63 | 0.2741 | 0.875 |
| 0.0595 | 64.0 | 64 | 0.2728 | 0.875 |
| 0.0595 | 65.0 | 65 | 0.2725 | 0.875 |
| 0.0595 | 66.0 | 66 | 0.2714 | 0.875 |
| 0.0595 | 67.0 | 67 | 0.2707 | 0.875 |
| 0.0595 | 68.0 | 68 | 0.2710 | 0.875 |
| 0.0595 | 69.0 | 69 | 0.2717 | 0.875 |
| 0.0512 | 70.0 | 70 | 0.2730 | 0.875 |
| 0.0512 | 71.0 | 71 | 0.2744 | 0.875 |
| 0.0512 | 72.0 | 72 | 0.2770 | 0.875 |
| 0.0512 | 73.0 | 73 | 0.2799 | 0.875 |
| 0.0512 | 74.0 | 74 | 0.2819 | 0.875 |
| 0.0512 | 75.0 | 75 | 0.2848 | 0.875 |
| 0.0512 | 76.0 | 76 | 0.2876 | 0.875 |
| 0.0512 | 77.0 | 77 | 0.2896 | 0.875 |
| 0.0512 | 78.0 | 78 | 0.2917 | 0.875 |
| 0.0512 | 79.0 | 79 | 0.2941 | 0.875 |
| 0.0434 | 80.0 | 80 | 0.2939 | 0.875 |
| 0.0434 | 81.0 | 81 | 0.2912 | 0.875 |
| 0.0434 | 82.0 | 82 | 0.2886 | 0.875 |
| 0.0434 | 83.0 | 83 | 0.2858 | 0.875 |
| 0.0434 | 84.0 | 84 | 0.2824 | 0.875 |
| 0.0434 | 85.0 | 85 | 0.2793 | 0.875 |
| 0.0434 | 86.0 | 86 | 0.2744 | 0.875 |
| 0.0434 | 87.0 | 87 | 0.2724 | 0.875 |
| 0.0434 | 88.0 | 88 | 0.2710 | 0.875 |
| 0.0434 | 89.0 | 89 | 0.2697 | 0.875 |
| 0.0369 | 90.0 | 90 | 0.2690 | 0.875 |
| 0.0369 | 91.0 | 91 | 0.2681 | 0.875 |
| 0.0369 | 92.0 | 92 | 0.2665 | 0.875 |
| 0.0369 | 93.0 | 93 | 0.2653 | 0.875 |
| 0.0369 | 94.0 | 94 | 0.2647 | 0.875 |
| 0.0369 | 95.0 | 95 | 0.2633 | 0.875 |
| 0.0369 | 96.0 | 96 | 0.2627 | 0.875 |
| 0.0369 | 97.0 | 97 | 0.2625 | 0.875 |
| 0.0369 | 98.0 | 98 | 0.2644 | 0.875 |
| 0.0369 | 99.0 | 99 | 0.2635 | 0.875 |
| 0.0322 | 100.0 | 100 | 0.2641 | 0.875 |
| 0.0322 | 101.0 | 101 | 0.2578 | 0.875 |
| 0.0322 | 102.0 | 102 | 0.2545 | 0.875 |
| 0.0322 | 103.0 | 103 | 0.2523 | 0.875 |
| 0.0322 | 104.0 | 104 | 0.2487 | 0.875 |
| 0.0322 | 105.0 | 105 | 0.2455 | 0.875 |
| 0.0322 | 106.0 | 106 | 0.2446 | 0.875 |
| 0.0322 | 107.0 | 107 | 0.2448 | 0.875 |
| 0.0322 | 108.0 | 108 | 0.2457 | 0.875 |
| 0.0322 | 109.0 | 109 | 0.2491 | 0.875 |
| 0.029 | 110.0 | 110 | 0.2533 | 0.875 |
| 0.029 | 111.0 | 111 | 0.2583 | 0.875 |
| 0.029 | 112.0 | 112 | 0.2636 | 0.875 |
| 0.029 | 113.0 | 113 | 0.2695 | 0.875 |
| 0.029 | 114.0 | 114 | 0.2741 | 0.875 |
| 0.029 | 115.0 | 115 | 0.2807 | 0.8438 |
| 0.029 | 116.0 | 116 | 0.2901 | 0.8438 |
| 0.029 | 117.0 | 117 | 0.2972 | 0.8438 |
| 0.029 | 118.0 | 118 | 0.3048 | 0.8438 |
| 0.029 | 119.0 | 119 | 0.3109 | 0.8438 |
| 0.025 | 120.0 | 120 | 0.3177 | 0.8438 |
| 0.025 | 121.0 | 121 | 0.3216 | 0.8438 |
| 0.025 | 122.0 | 122 | 0.3244 | 0.8438 |
| 0.025 | 123.0 | 123 | 0.3253 | 0.8438 |
| 0.025 | 124.0 | 124 | 0.3263 | 0.8438 |
| 0.025 | 125.0 | 125 | 0.3257 | 0.8438 |
| 0.025 | 126.0 | 126 | 0.3258 | 0.8438 |
| 0.025 | 127.0 | 127 | 0.3259 | 0.8438 |
| 0.025 | 128.0 | 128 | 0.3269 | 0.8438 |
| 0.025 | 129.0 | 129 | 0.3269 | 0.8125 |
| 0.0213 | 130.0 | 130 | 0.3278 | 0.8125 |
| 0.0213 | 131.0 | 131 | 0.3265 | 0.8125 |
| 0.0213 | 132.0 | 132 | 0.3268 | 0.8125 |
| 0.0213 | 133.0 | 133 | 0.3242 | 0.8125 |
| 0.0213 | 134.0 | 134 | 0.3193 | 0.8438 |
| 0.0213 | 135.0 | 135 | 0.3127 | 0.8438 |
| 0.0213 | 136.0 | 136 | 0.3047 | 0.8438 |
| 0.0213 | 137.0 | 137 | 0.2973 | 0.8438 |
| 0.0213 | 138.0 | 138 | 0.2891 | 0.8438 |
| 0.0213 | 139.0 | 139 | 0.2836 | 0.8438 |
| 0.0196 | 140.0 | 140 | 0.2794 | 0.8438 |
| 0.0196 | 141.0 | 141 | 0.2769 | 0.8438 |
| 0.0196 | 142.0 | 142 | 0.2762 | 0.8438 |
| 0.0196 | 143.0 | 143 | 0.2764 | 0.8438 |
| 0.0196 | 144.0 | 144 | 0.2776 | 0.8438 |
| 0.0196 | 145.0 | 145 | 0.2806 | 0.8438 |
| 0.0196 | 146.0 | 146 | 0.2858 | 0.8438 |
| 0.0196 | 147.0 | 147 | 0.2876 | 0.8438 |
| 0.0196 | 148.0 | 148 | 0.2899 | 0.8438 |
| 0.0196 | 149.0 | 149 | 0.2893 | 0.8438 |
| 0.0171 | 150.0 | 150 | 0.2887 | 0.8438 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
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