--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-yelp_polarity-16-100 results: [] --- # best_model-yelp_polarity-16-100 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3649 - Accuracy: 0.9375 ## 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 | 0.4545 | 0.9375 | | No log | 2.0 | 2 | 0.4554 | 0.9375 | | No log | 3.0 | 3 | 0.4547 | 0.9375 | | No log | 4.0 | 4 | 0.4527 | 0.9375 | | No log | 5.0 | 5 | 0.4500 | 0.9375 | | No log | 6.0 | 6 | 0.4459 | 0.9375 | | No log | 7.0 | 7 | 0.4399 | 0.9375 | | No log | 8.0 | 8 | 0.4325 | 0.9375 | | No log | 9.0 | 9 | 0.4229 | 0.9375 | | 0.0615 | 10.0 | 10 | 0.4163 | 0.9375 | | 0.0615 | 11.0 | 11 | 0.4128 | 0.9375 | | 0.0615 | 12.0 | 12 | 0.4064 | 0.9375 | | 0.0615 | 13.0 | 13 | 0.3967 | 0.9375 | | 0.0615 | 14.0 | 14 | 0.3834 | 0.9375 | | 0.0615 | 15.0 | 15 | 0.3664 | 0.9375 | | 0.0615 | 16.0 | 16 | 0.3437 | 0.9375 | | 0.0615 | 17.0 | 17 | 0.3272 | 0.9375 | | 0.0615 | 18.0 | 18 | 0.3177 | 0.9375 | | 0.0615 | 19.0 | 19 | 0.3141 | 0.9375 | | 0.0434 | 20.0 | 20 | 0.3169 | 0.9375 | | 0.0434 | 21.0 | 21 | 0.3263 | 0.9375 | | 0.0434 | 22.0 | 22 | 0.3365 | 0.9375 | | 0.0434 | 23.0 | 23 | 0.3472 | 0.9375 | | 0.0434 | 24.0 | 24 | 0.3639 | 0.9375 | | 0.0434 | 25.0 | 25 | 0.3799 | 0.9375 | | 0.0434 | 26.0 | 26 | 0.3938 | 0.9375 | | 0.0434 | 27.0 | 27 | 0.4059 | 0.9375 | | 0.0434 | 28.0 | 28 | 0.4103 | 0.9375 | | 0.0434 | 29.0 | 29 | 0.4072 | 0.9375 | | 0.0006 | 30.0 | 30 | 0.4046 | 0.9375 | | 0.0006 | 31.0 | 31 | 0.4023 | 0.9375 | | 0.0006 | 32.0 | 32 | 0.4003 | 0.9375 | | 0.0006 | 33.0 | 33 | 0.3990 | 0.9375 | | 0.0006 | 34.0 | 34 | 0.3979 | 0.9375 | | 0.0006 | 35.0 | 35 | 0.3969 | 0.9375 | | 0.0006 | 36.0 | 36 | 0.3961 | 0.9375 | | 0.0006 | 37.0 | 37 | 0.3955 | 0.9375 | | 0.0006 | 38.0 | 38 | 0.3951 | 0.9375 | | 0.0006 | 39.0 | 39 | 0.3954 | 0.9375 | | 0.0003 | 40.0 | 40 | 0.3960 | 0.9375 | | 0.0003 | 41.0 | 41 | 0.3961 | 0.9375 | | 0.0003 | 42.0 | 42 | 0.3949 | 0.9375 | | 0.0003 | 43.0 | 43 | 0.3912 | 0.9375 | | 0.0003 | 44.0 | 44 | 0.3875 | 0.9375 | | 0.0003 | 45.0 | 45 | 0.3851 | 0.9375 | | 0.0003 | 46.0 | 46 | 0.3833 | 0.9375 | | 0.0003 | 47.0 | 47 | 0.3822 | 0.9375 | | 0.0003 | 48.0 | 48 | 0.3812 | 0.9375 | | 0.0003 | 49.0 | 49 | 0.3807 | 0.9375 | | 0.0003 | 50.0 | 50 | 0.3805 | 0.9375 | | 0.0003 | 51.0 | 51 | 0.3807 | 0.9375 | | 0.0003 | 52.0 | 52 | 0.3812 | 0.9375 | | 0.0003 | 53.0 | 53 | 0.3820 | 0.9375 | | 0.0003 | 54.0 | 54 | 0.3830 | 0.9375 | | 0.0003 | 55.0 | 55 | 0.3841 | 0.9375 | | 0.0003 | 56.0 | 56 | 0.3859 | 0.9375 | | 0.0003 | 57.0 | 57 | 0.3885 | 0.9375 | | 0.0003 | 58.0 | 58 | 0.3923 | 0.9375 | | 0.0003 | 59.0 | 59 | 0.3958 | 0.9375 | | 0.0003 | 60.0 | 60 | 0.3992 | 0.9375 | | 0.0003 | 61.0 | 61 | 0.4026 | 0.9375 | | 0.0003 | 62.0 | 62 | 0.4059 | 0.9375 | | 0.0003 | 63.0 | 63 | 0.4093 | 0.9375 | | 0.0003 | 64.0 | 64 | 0.4125 | 0.9375 | | 0.0003 | 65.0 | 65 | 0.4152 | 0.9375 | | 0.0003 | 66.0 | 66 | 0.4179 | 0.9375 | | 0.0003 | 67.0 | 67 | 0.4207 | 0.9375 | | 0.0003 | 68.0 | 68 | 0.4234 | 0.9375 | | 0.0003 | 69.0 | 69 | 0.4291 | 0.9375 | | 0.0002 | 70.0 | 70 | 0.4345 | 0.9375 | | 0.0002 | 71.0 | 71 | 0.4392 | 0.9375 | | 0.0002 | 72.0 | 72 | 0.4434 | 0.9375 | | 0.0002 | 73.0 | 73 | 0.4568 | 0.9375 | | 0.0002 | 74.0 | 74 | 0.4678 | 0.9375 | | 0.0002 | 75.0 | 75 | 0.4775 | 0.9375 | | 0.0002 | 76.0 | 76 | 0.4831 | 0.9375 | | 0.0002 | 77.0 | 77 | 0.4880 | 0.9375 | | 0.0002 | 78.0 | 78 | 0.4925 | 0.9375 | | 0.0002 | 79.0 | 79 | 0.4964 | 0.9375 | | 0.0002 | 80.0 | 80 | 0.4984 | 0.9375 | | 0.0002 | 81.0 | 81 | 0.4999 | 0.9375 | | 0.0002 | 82.0 | 82 | 0.5013 | 0.9375 | | 0.0002 | 83.0 | 83 | 0.5027 | 0.9375 | | 0.0002 | 84.0 | 84 | 0.5039 | 0.9375 | | 0.0002 | 85.0 | 85 | 0.5050 | 0.9375 | | 0.0002 | 86.0 | 86 | 0.5061 | 0.9375 | | 0.0002 | 87.0 | 87 | 0.5071 | 0.9375 | | 0.0002 | 88.0 | 88 | 0.5081 | 0.9375 | | 0.0002 | 89.0 | 89 | 0.5090 | 0.9375 | | 0.0002 | 90.0 | 90 | 0.5099 | 0.9375 | | 0.0002 | 91.0 | 91 | 0.5102 | 0.9375 | | 0.0002 | 92.0 | 92 | 0.5105 | 0.9375 | | 0.0002 | 93.0 | 93 | 0.5109 | 0.9375 | | 0.0002 | 94.0 | 94 | 0.5114 | 0.9375 | | 0.0002 | 95.0 | 95 | 0.5115 | 0.9375 | | 0.0002 | 96.0 | 96 | 0.5117 | 0.9375 | | 0.0002 | 97.0 | 97 | 0.4927 | 0.9375 | | 0.0002 | 98.0 | 98 | 0.4685 | 0.9375 | | 0.0002 | 99.0 | 99 | 0.4380 | 0.9375 | | 0.0003 | 100.0 | 100 | 0.4010 | 0.9375 | | 0.0003 | 101.0 | 101 | 0.3594 | 0.9375 | | 0.0003 | 102.0 | 102 | 0.3201 | 0.9375 | | 0.0003 | 103.0 | 103 | 0.2908 | 0.9375 | | 0.0003 | 104.0 | 104 | 0.2745 | 0.9688 | | 0.0003 | 105.0 | 105 | 0.2665 | 0.9688 | | 0.0003 | 106.0 | 106 | 0.2624 | 0.9688 | | 0.0003 | 107.0 | 107 | 0.2597 | 0.9688 | | 0.0003 | 108.0 | 108 | 0.2575 | 0.9688 | | 0.0003 | 109.0 | 109 | 0.2558 | 0.9688 | | 0.0002 | 110.0 | 110 | 0.2544 | 0.9688 | | 0.0002 | 111.0 | 111 | 0.2531 | 0.9688 | | 0.0002 | 112.0 | 112 | 0.2521 | 0.9688 | | 0.0002 | 113.0 | 113 | 0.2513 | 0.9688 | | 0.0002 | 114.0 | 114 | 0.2506 | 0.9688 | | 0.0002 | 115.0 | 115 | 0.2502 | 0.9688 | | 0.0002 | 116.0 | 116 | 0.2501 | 0.9688 | | 0.0002 | 117.0 | 117 | 0.2500 | 0.9688 | | 0.0002 | 118.0 | 118 | 0.2501 | 0.9688 | | 0.0002 | 119.0 | 119 | 0.2503 | 0.9688 | | 0.0001 | 120.0 | 120 | 0.2505 | 0.9688 | | 0.0001 | 121.0 | 121 | 0.2532 | 0.9688 | | 0.0001 | 122.0 | 122 | 0.2560 | 0.9688 | | 0.0001 | 123.0 | 123 | 0.2585 | 0.9688 | | 0.0001 | 124.0 | 124 | 0.2608 | 0.9688 | | 0.0001 | 125.0 | 125 | 0.2630 | 0.9688 | | 0.0001 | 126.0 | 126 | 0.2654 | 0.9688 | | 0.0001 | 127.0 | 127 | 0.2676 | 0.9688 | | 0.0001 | 128.0 | 128 | 0.2696 | 0.9688 | | 0.0001 | 129.0 | 129 | 0.2717 | 0.9688 | | 0.0002 | 130.0 | 130 | 0.2737 | 0.9688 | | 0.0002 | 131.0 | 131 | 0.2759 | 0.9688 | | 0.0002 | 132.0 | 132 | 0.2783 | 0.9688 | | 0.0002 | 133.0 | 133 | 0.2808 | 0.9688 | | 0.0002 | 134.0 | 134 | 0.2837 | 0.9688 | | 0.0002 | 135.0 | 135 | 0.2871 | 0.9688 | | 0.0002 | 136.0 | 136 | 0.2908 | 0.9688 | | 0.0002 | 137.0 | 137 | 0.2950 | 0.9688 | | 0.0002 | 138.0 | 138 | 0.2995 | 0.9688 | | 0.0002 | 139.0 | 139 | 0.3043 | 0.9375 | | 0.0001 | 140.0 | 140 | 0.3094 | 0.9375 | | 0.0001 | 141.0 | 141 | 0.3147 | 0.9375 | | 0.0001 | 142.0 | 142 | 0.3201 | 0.9375 | | 0.0001 | 143.0 | 143 | 0.3257 | 0.9375 | | 0.0001 | 144.0 | 144 | 0.3316 | 0.9375 | | 0.0001 | 145.0 | 145 | 0.3375 | 0.9375 | | 0.0001 | 146.0 | 146 | 0.3434 | 0.9375 | | 0.0001 | 147.0 | 147 | 0.3492 | 0.9375 | | 0.0001 | 148.0 | 148 | 0.3547 | 0.9375 | | 0.0001 | 149.0 | 149 | 0.3599 | 0.9375 | | 0.0001 | 150.0 | 150 | 0.3649 | 0.9375 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3