<!-- 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. -->
bert-base-uncased-sst-2-16-42
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4392
- 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 | 0.5342 | 0.6875 |
| No log | 2.0 | 2 | 0.5341 | 0.6875 |
| No log | 3.0 | 3 | 0.5340 | 0.6875 |
| No log | 4.0 | 4 | 0.5339 | 0.6875 |
| No log | 5.0 | 5 | 0.5337 | 0.6875 |
| No log | 6.0 | 6 | 0.5335 | 0.6875 |
| No log | 7.0 | 7 | 0.5332 | 0.6875 |
| No log | 8.0 | 8 | 0.5328 | 0.6875 |
| No log | 9.0 | 9 | 0.5325 | 0.6875 |
| 0.5587 | 10.0 | 10 | 0.5320 | 0.6875 |
| 0.5587 | 11.0 | 11 | 0.5314 | 0.6875 |
| 0.5587 | 12.0 | 12 | 0.5305 | 0.6875 |
| 0.5587 | 13.0 | 13 | 0.5296 | 0.6875 |
| 0.5587 | 14.0 | 14 | 0.5285 | 0.6875 |
| 0.5587 | 15.0 | 15 | 0.5274 | 0.6875 |
| 0.5587 | 16.0 | 16 | 0.5264 | 0.6875 |
| 0.5587 | 17.0 | 17 | 0.5254 | 0.6875 |
| 0.5587 | 18.0 | 18 | 0.5244 | 0.6875 |
| 0.5587 | 19.0 | 19 | 0.5235 | 0.6875 |
| 0.5357 | 20.0 | 20 | 0.5225 | 0.6875 |
| 0.5357 | 21.0 | 21 | 0.5216 | 0.6875 |
| 0.5357 | 22.0 | 22 | 0.5204 | 0.6875 |
| 0.5357 | 23.0 | 23 | 0.5192 | 0.6875 |
| 0.5357 | 24.0 | 24 | 0.5179 | 0.6875 |
| 0.5357 | 25.0 | 25 | 0.5165 | 0.6875 |
| 0.5357 | 26.0 | 26 | 0.5151 | 0.7188 |
| 0.5357 | 27.0 | 27 | 0.5135 | 0.7188 |
| 0.5357 | 28.0 | 28 | 0.5119 | 0.7188 |
| 0.5357 | 29.0 | 29 | 0.5102 | 0.7188 |
| 0.4826 | 30.0 | 30 | 0.5085 | 0.7188 |
| 0.4826 | 31.0 | 31 | 0.5064 | 0.7188 |
| 0.4826 | 32.0 | 32 | 0.5050 | 0.7188 |
| 0.4826 | 33.0 | 33 | 0.5036 | 0.7188 |
| 0.4826 | 34.0 | 34 | 0.5019 | 0.7188 |
| 0.4826 | 35.0 | 35 | 0.5002 | 0.7188 |
| 0.4826 | 36.0 | 36 | 0.4980 | 0.7188 |
| 0.4826 | 37.0 | 37 | 0.4958 | 0.7188 |
| 0.4826 | 38.0 | 38 | 0.4931 | 0.7188 |
| 0.4826 | 39.0 | 39 | 0.4900 | 0.7188 |
| 0.438 | 40.0 | 40 | 0.4866 | 0.75 |
| 0.438 | 41.0 | 41 | 0.4831 | 0.75 |
| 0.438 | 42.0 | 42 | 0.4802 | 0.75 |
| 0.438 | 43.0 | 43 | 0.4773 | 0.75 |
| 0.438 | 44.0 | 44 | 0.4746 | 0.75 |
| 0.438 | 45.0 | 45 | 0.4713 | 0.7812 |
| 0.438 | 46.0 | 46 | 0.4685 | 0.7812 |
| 0.438 | 47.0 | 47 | 0.4651 | 0.7812 |
| 0.438 | 48.0 | 48 | 0.4620 | 0.7812 |
| 0.438 | 49.0 | 49 | 0.4583 | 0.7812 |
| 0.367 | 50.0 | 50 | 0.4552 | 0.7812 |
| 0.367 | 51.0 | 51 | 0.4533 | 0.7812 |
| 0.367 | 52.0 | 52 | 0.4519 | 0.7812 |
| 0.367 | 53.0 | 53 | 0.4500 | 0.7812 |
| 0.367 | 54.0 | 54 | 0.4482 | 0.7812 |
| 0.367 | 55.0 | 55 | 0.4470 | 0.7812 |
| 0.367 | 56.0 | 56 | 0.4460 | 0.7812 |
| 0.367 | 57.0 | 57 | 0.4452 | 0.7812 |
| 0.367 | 58.0 | 58 | 0.4440 | 0.7812 |
| 0.367 | 59.0 | 59 | 0.4422 | 0.7812 |
| 0.2811 | 60.0 | 60 | 0.4401 | 0.7812 |
| 0.2811 | 61.0 | 61 | 0.4391 | 0.7812 |
| 0.2811 | 62.0 | 62 | 0.4370 | 0.7812 |
| 0.2811 | 63.0 | 63 | 0.4358 | 0.7812 |
| 0.2811 | 64.0 | 64 | 0.4342 | 0.7812 |
| 0.2811 | 65.0 | 65 | 0.4338 | 0.7812 |
| 0.2811 | 66.0 | 66 | 0.4339 | 0.7812 |
| 0.2811 | 67.0 | 67 | 0.4345 | 0.7812 |
| 0.2811 | 68.0 | 68 | 0.4339 | 0.7812 |
| 0.2811 | 69.0 | 69 | 0.4339 | 0.75 |
| 0.22 | 70.0 | 70 | 0.4347 | 0.7812 |
| 0.22 | 71.0 | 71 | 0.4342 | 0.7812 |
| 0.22 | 72.0 | 72 | 0.4338 | 0.7812 |
| 0.22 | 73.0 | 73 | 0.4335 | 0.7812 |
| 0.22 | 74.0 | 74 | 0.4322 | 0.7812 |
| 0.22 | 75.0 | 75 | 0.4296 | 0.7812 |
| 0.22 | 76.0 | 76 | 0.4266 | 0.8125 |
| 0.22 | 77.0 | 77 | 0.4230 | 0.8438 |
| 0.22 | 78.0 | 78 | 0.4199 | 0.8438 |
| 0.22 | 79.0 | 79 | 0.4170 | 0.8438 |
| 0.1839 | 80.0 | 80 | 0.4147 | 0.8438 |
| 0.1839 | 81.0 | 81 | 0.4131 | 0.8438 |
| 0.1839 | 82.0 | 82 | 0.4120 | 0.8438 |
| 0.1839 | 83.0 | 83 | 0.4102 | 0.8438 |
| 0.1839 | 84.0 | 84 | 0.4090 | 0.8438 |
| 0.1839 | 85.0 | 85 | 0.4073 | 0.8438 |
| 0.1839 | 86.0 | 86 | 0.4059 | 0.8438 |
| 0.1839 | 87.0 | 87 | 0.4049 | 0.8438 |
| 0.1839 | 88.0 | 88 | 0.4043 | 0.8438 |
| 0.1839 | 89.0 | 89 | 0.4044 | 0.8438 |
| 0.1385 | 90.0 | 90 | 0.4045 | 0.8438 |
| 0.1385 | 91.0 | 91 | 0.4049 | 0.8438 |
| 0.1385 | 92.0 | 92 | 0.4054 | 0.8438 |
| 0.1385 | 93.0 | 93 | 0.4059 | 0.8438 |
| 0.1385 | 94.0 | 94 | 0.4057 | 0.8125 |
| 0.1385 | 95.0 | 95 | 0.4066 | 0.8125 |
| 0.1385 | 96.0 | 96 | 0.4070 | 0.8125 |
| 0.1385 | 97.0 | 97 | 0.4072 | 0.8125 |
| 0.1385 | 98.0 | 98 | 0.4078 | 0.8125 |
| 0.1385 | 99.0 | 99 | 0.4081 | 0.8125 |
| 0.1178 | 100.0 | 100 | 0.4079 | 0.8125 |
| 0.1178 | 101.0 | 101 | 0.4083 | 0.8125 |
| 0.1178 | 102.0 | 102 | 0.4087 | 0.8125 |
| 0.1178 | 103.0 | 103 | 0.4101 | 0.8125 |
| 0.1178 | 104.0 | 104 | 0.4120 | 0.8125 |
| 0.1178 | 105.0 | 105 | 0.4132 | 0.8125 |
| 0.1178 | 106.0 | 106 | 0.4148 | 0.8125 |
| 0.1178 | 107.0 | 107 | 0.4163 | 0.8125 |
| 0.1178 | 108.0 | 108 | 0.4176 | 0.8125 |
| 0.1178 | 109.0 | 109 | 0.4207 | 0.8125 |
| 0.0987 | 110.0 | 110 | 0.4235 | 0.8125 |
| 0.0987 | 111.0 | 111 | 0.4253 | 0.8125 |
| 0.0987 | 112.0 | 112 | 0.4268 | 0.8125 |
| 0.0987 | 113.0 | 113 | 0.4273 | 0.8125 |
| 0.0987 | 114.0 | 114 | 0.4271 | 0.8125 |
| 0.0987 | 115.0 | 115 | 0.4270 | 0.8125 |
| 0.0987 | 116.0 | 116 | 0.4277 | 0.8125 |
| 0.0987 | 117.0 | 117 | 0.4279 | 0.8125 |
| 0.0987 | 118.0 | 118 | 0.4287 | 0.8125 |
| 0.0987 | 119.0 | 119 | 0.4264 | 0.8125 |
| 0.0788 | 120.0 | 120 | 0.4252 | 0.8125 |
| 0.0788 | 121.0 | 121 | 0.4234 | 0.8125 |
| 0.0788 | 122.0 | 122 | 0.4213 | 0.8125 |
| 0.0788 | 123.0 | 123 | 0.4184 | 0.8125 |
| 0.0788 | 124.0 | 124 | 0.4164 | 0.8125 |
| 0.0788 | 125.0 | 125 | 0.4148 | 0.8125 |
| 0.0788 | 126.0 | 126 | 0.4140 | 0.8125 |
| 0.0788 | 127.0 | 127 | 0.4142 | 0.8125 |
| 0.0788 | 128.0 | 128 | 0.4135 | 0.8125 |
| 0.0788 | 129.0 | 129 | 0.4132 | 0.8125 |
| 0.0612 | 130.0 | 130 | 0.4131 | 0.8125 |
| 0.0612 | 131.0 | 131 | 0.4136 | 0.8125 |
| 0.0612 | 132.0 | 132 | 0.4144 | 0.8125 |
| 0.0612 | 133.0 | 133 | 0.4148 | 0.8125 |
| 0.0612 | 134.0 | 134 | 0.4154 | 0.8125 |
| 0.0612 | 135.0 | 135 | 0.4167 | 0.8125 |
| 0.0612 | 136.0 | 136 | 0.4181 | 0.8125 |
| 0.0612 | 137.0 | 137 | 0.4199 | 0.8125 |
| 0.0612 | 138.0 | 138 | 0.4211 | 0.8125 |
| 0.0612 | 139.0 | 139 | 0.4222 | 0.8125 |
| 0.0466 | 140.0 | 140 | 0.4243 | 0.8125 |
| 0.0466 | 141.0 | 141 | 0.4256 | 0.8125 |
| 0.0466 | 142.0 | 142 | 0.4278 | 0.8125 |
| 0.0466 | 143.0 | 143 | 0.4280 | 0.8125 |
| 0.0466 | 144.0 | 144 | 0.4286 | 0.8125 |
| 0.0466 | 145.0 | 145 | 0.4294 | 0.8125 |
| 0.0466 | 146.0 | 146 | 0.4311 | 0.8125 |
| 0.0466 | 147.0 | 147 | 0.4332 | 0.8125 |
| 0.0466 | 148.0 | 148 | 0.4351 | 0.7812 |
| 0.0466 | 149.0 | 149 | 0.4371 | 0.7812 |
| 0.036 | 150.0 | 150 | 0.4392 | 0.7812 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
