|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: best_model-sst-2-32-13 |
|
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-2-32-13 |
|
|
|
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: 1.2039 |
|
- Accuracy: 0.8281 |
|
|
|
## 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 | 2 | 0.7856 | 0.8125 | |
|
| No log | 2.0 | 4 | 0.7856 | 0.8125 | |
|
| No log | 3.0 | 6 | 0.7857 | 0.8125 | |
|
| No log | 4.0 | 8 | 0.7862 | 0.8125 | |
|
| 0.5036 | 5.0 | 10 | 0.7867 | 0.8125 | |
|
| 0.5036 | 6.0 | 12 | 0.7873 | 0.8125 | |
|
| 0.5036 | 7.0 | 14 | 0.7883 | 0.8125 | |
|
| 0.5036 | 8.0 | 16 | 0.7908 | 0.8125 | |
|
| 0.5036 | 9.0 | 18 | 0.7955 | 0.8281 | |
|
| 0.4185 | 10.0 | 20 | 0.8014 | 0.8125 | |
|
| 0.4185 | 11.0 | 22 | 0.8066 | 0.8125 | |
|
| 0.4185 | 12.0 | 24 | 0.8128 | 0.8125 | |
|
| 0.4185 | 13.0 | 26 | 0.8208 | 0.8125 | |
|
| 0.4185 | 14.0 | 28 | 0.8292 | 0.8125 | |
|
| 0.2904 | 15.0 | 30 | 0.8390 | 0.8281 | |
|
| 0.2904 | 16.0 | 32 | 0.8441 | 0.8125 | |
|
| 0.2904 | 17.0 | 34 | 0.8451 | 0.8125 | |
|
| 0.2904 | 18.0 | 36 | 0.8484 | 0.8125 | |
|
| 0.2904 | 19.0 | 38 | 0.8510 | 0.8125 | |
|
| 0.257 | 20.0 | 40 | 0.8506 | 0.8125 | |
|
| 0.257 | 21.0 | 42 | 0.8471 | 0.8125 | |
|
| 0.257 | 22.0 | 44 | 0.8397 | 0.8125 | |
|
| 0.257 | 23.0 | 46 | 0.8311 | 0.8281 | |
|
| 0.257 | 24.0 | 48 | 0.8248 | 0.8281 | |
|
| 0.2216 | 25.0 | 50 | 0.8175 | 0.8281 | |
|
| 0.2216 | 26.0 | 52 | 0.8108 | 0.8281 | |
|
| 0.2216 | 27.0 | 54 | 0.8012 | 0.8281 | |
|
| 0.2216 | 28.0 | 56 | 0.7907 | 0.8281 | |
|
| 0.2216 | 29.0 | 58 | 0.7851 | 0.8281 | |
|
| 0.1811 | 30.0 | 60 | 0.7800 | 0.8281 | |
|
| 0.1811 | 31.0 | 62 | 0.7713 | 0.8281 | |
|
| 0.1811 | 32.0 | 64 | 0.7620 | 0.8281 | |
|
| 0.1811 | 33.0 | 66 | 0.7502 | 0.8125 | |
|
| 0.1811 | 34.0 | 68 | 0.7386 | 0.8125 | |
|
| 0.1015 | 35.0 | 70 | 0.7320 | 0.8125 | |
|
| 0.1015 | 36.0 | 72 | 0.7296 | 0.8125 | |
|
| 0.1015 | 37.0 | 74 | 0.7315 | 0.8125 | |
|
| 0.1015 | 38.0 | 76 | 0.7371 | 0.8281 | |
|
| 0.1015 | 39.0 | 78 | 0.7442 | 0.8281 | |
|
| 0.0725 | 40.0 | 80 | 0.7475 | 0.8281 | |
|
| 0.0725 | 41.0 | 82 | 0.7474 | 0.8281 | |
|
| 0.0725 | 42.0 | 84 | 0.7479 | 0.8281 | |
|
| 0.0725 | 43.0 | 86 | 0.7501 | 0.8281 | |
|
| 0.0725 | 44.0 | 88 | 0.7523 | 0.8281 | |
|
| 0.0249 | 45.0 | 90 | 0.7491 | 0.8281 | |
|
| 0.0249 | 46.0 | 92 | 0.7537 | 0.8281 | |
|
| 0.0249 | 47.0 | 94 | 0.7615 | 0.8281 | |
|
| 0.0249 | 48.0 | 96 | 0.7767 | 0.8281 | |
|
| 0.0249 | 49.0 | 98 | 0.7909 | 0.8281 | |
|
| 0.0071 | 50.0 | 100 | 0.8011 | 0.8281 | |
|
| 0.0071 | 51.0 | 102 | 0.8145 | 0.8281 | |
|
| 0.0071 | 52.0 | 104 | 0.8286 | 0.8281 | |
|
| 0.0071 | 53.0 | 106 | 0.8415 | 0.8281 | |
|
| 0.0071 | 54.0 | 108 | 0.8451 | 0.8281 | |
|
| 0.0057 | 55.0 | 110 | 0.8438 | 0.8281 | |
|
| 0.0057 | 56.0 | 112 | 0.8368 | 0.8281 | |
|
| 0.0057 | 57.0 | 114 | 0.8340 | 0.8281 | |
|
| 0.0057 | 58.0 | 116 | 0.8431 | 0.8281 | |
|
| 0.0057 | 59.0 | 118 | 0.8509 | 0.8281 | |
|
| 0.0052 | 60.0 | 120 | 0.8579 | 0.8281 | |
|
| 0.0052 | 61.0 | 122 | 0.8640 | 0.8281 | |
|
| 0.0052 | 62.0 | 124 | 0.8691 | 0.8281 | |
|
| 0.0052 | 63.0 | 126 | 0.8733 | 0.8281 | |
|
| 0.0052 | 64.0 | 128 | 0.8767 | 0.8281 | |
|
| 0.0027 | 65.0 | 130 | 0.8800 | 0.8281 | |
|
| 0.0027 | 66.0 | 132 | 0.8826 | 0.8281 | |
|
| 0.0027 | 67.0 | 134 | 0.8865 | 0.8281 | |
|
| 0.0027 | 68.0 | 136 | 0.8929 | 0.8281 | |
|
| 0.0027 | 69.0 | 138 | 0.9006 | 0.8281 | |
|
| 0.0025 | 70.0 | 140 | 0.9079 | 0.8281 | |
|
| 0.0025 | 71.0 | 142 | 0.9226 | 0.8281 | |
|
| 0.0025 | 72.0 | 144 | 0.9417 | 0.8281 | |
|
| 0.0025 | 73.0 | 146 | 0.9560 | 0.8281 | |
|
| 0.0025 | 74.0 | 148 | 0.9663 | 0.8281 | |
|
| 0.0028 | 75.0 | 150 | 0.9737 | 0.8125 | |
|
| 0.0028 | 76.0 | 152 | 0.9761 | 0.8281 | |
|
| 0.0028 | 77.0 | 154 | 0.9724 | 0.8281 | |
|
| 0.0028 | 78.0 | 156 | 0.9675 | 0.8281 | |
|
| 0.0028 | 79.0 | 158 | 0.9602 | 0.8281 | |
|
| 0.0029 | 80.0 | 160 | 0.9534 | 0.8281 | |
|
| 0.0029 | 81.0 | 162 | 0.9478 | 0.8281 | |
|
| 0.0029 | 82.0 | 164 | 0.9437 | 0.8281 | |
|
| 0.0029 | 83.0 | 166 | 0.9400 | 0.8281 | |
|
| 0.0029 | 84.0 | 168 | 0.9366 | 0.8281 | |
|
| 0.0016 | 85.0 | 170 | 0.9346 | 0.8281 | |
|
| 0.0016 | 86.0 | 172 | 0.9343 | 0.8281 | |
|
| 0.0016 | 87.0 | 174 | 0.9353 | 0.8281 | |
|
| 0.0016 | 88.0 | 176 | 0.9367 | 0.8281 | |
|
| 0.0016 | 89.0 | 178 | 0.9386 | 0.8281 | |
|
| 0.0015 | 90.0 | 180 | 0.9413 | 0.8281 | |
|
| 0.0015 | 91.0 | 182 | 0.9439 | 0.8281 | |
|
| 0.0015 | 92.0 | 184 | 0.9472 | 0.8281 | |
|
| 0.0015 | 93.0 | 186 | 0.9510 | 0.8281 | |
|
| 0.0015 | 94.0 | 188 | 0.9552 | 0.8281 | |
|
| 0.0013 | 95.0 | 190 | 0.9596 | 0.8281 | |
|
| 0.0013 | 96.0 | 192 | 0.9641 | 0.8281 | |
|
| 0.0013 | 97.0 | 194 | 0.9684 | 0.8281 | |
|
| 0.0013 | 98.0 | 196 | 0.9725 | 0.8281 | |
|
| 0.0013 | 99.0 | 198 | 0.9777 | 0.8281 | |
|
| 0.0012 | 100.0 | 200 | 0.9881 | 0.8281 | |
|
| 0.0012 | 101.0 | 202 | 0.9981 | 0.8281 | |
|
| 0.0012 | 102.0 | 204 | 1.0066 | 0.8281 | |
|
| 0.0012 | 103.0 | 206 | 1.0043 | 0.8281 | |
|
| 0.0012 | 104.0 | 208 | 1.0029 | 0.8281 | |
|
| 0.0011 | 105.0 | 210 | 1.0022 | 0.8281 | |
|
| 0.0011 | 106.0 | 212 | 1.0017 | 0.8281 | |
|
| 0.0011 | 107.0 | 214 | 1.0021 | 0.8281 | |
|
| 0.0011 | 108.0 | 216 | 1.0029 | 0.8281 | |
|
| 0.0011 | 109.0 | 218 | 1.0048 | 0.8281 | |
|
| 0.001 | 110.0 | 220 | 1.0069 | 0.8281 | |
|
| 0.001 | 111.0 | 222 | 1.0114 | 0.8281 | |
|
| 0.001 | 112.0 | 224 | 1.0171 | 0.8281 | |
|
| 0.001 | 113.0 | 226 | 1.0225 | 0.8281 | |
|
| 0.001 | 114.0 | 228 | 1.0273 | 0.8281 | |
|
| 0.0009 | 115.0 | 230 | 1.0325 | 0.8281 | |
|
| 0.0009 | 116.0 | 232 | 1.0375 | 0.8281 | |
|
| 0.0009 | 117.0 | 234 | 1.0419 | 0.8281 | |
|
| 0.0009 | 118.0 | 236 | 1.0460 | 0.8281 | |
|
| 0.0009 | 119.0 | 238 | 1.0500 | 0.8281 | |
|
| 0.0009 | 120.0 | 240 | 1.0538 | 0.8281 | |
|
| 0.0009 | 121.0 | 242 | 1.0572 | 0.8281 | |
|
| 0.0009 | 122.0 | 244 | 1.0611 | 0.8281 | |
|
| 0.0009 | 123.0 | 246 | 1.0650 | 0.8281 | |
|
| 0.0009 | 124.0 | 248 | 1.0664 | 0.8281 | |
|
| 0.0015 | 125.0 | 250 | 1.1047 | 0.8281 | |
|
| 0.0015 | 126.0 | 252 | 1.1348 | 0.8281 | |
|
| 0.0015 | 127.0 | 254 | 1.1568 | 0.8125 | |
|
| 0.0015 | 128.0 | 256 | 1.1730 | 0.8125 | |
|
| 0.0015 | 129.0 | 258 | 1.1849 | 0.8125 | |
|
| 0.0007 | 130.0 | 260 | 1.1937 | 0.8125 | |
|
| 0.0007 | 131.0 | 262 | 1.2006 | 0.8125 | |
|
| 0.0007 | 132.0 | 264 | 1.2057 | 0.8125 | |
|
| 0.0007 | 133.0 | 266 | 1.2096 | 0.8125 | |
|
| 0.0007 | 134.0 | 268 | 1.2120 | 0.8125 | |
|
| 0.0007 | 135.0 | 270 | 1.2140 | 0.8125 | |
|
| 0.0007 | 136.0 | 272 | 1.2134 | 0.8125 | |
|
| 0.0007 | 137.0 | 274 | 1.2122 | 0.8125 | |
|
| 0.0007 | 138.0 | 276 | 1.2105 | 0.8125 | |
|
| 0.0007 | 139.0 | 278 | 1.2089 | 0.8125 | |
|
| 0.0006 | 140.0 | 280 | 1.2075 | 0.8125 | |
|
| 0.0006 | 141.0 | 282 | 1.2063 | 0.8125 | |
|
| 0.0006 | 142.0 | 284 | 1.2054 | 0.8125 | |
|
| 0.0006 | 143.0 | 286 | 1.2049 | 0.8125 | |
|
| 0.0006 | 144.0 | 288 | 1.2039 | 0.8125 | |
|
| 0.0005 | 145.0 | 290 | 1.2032 | 0.8281 | |
|
| 0.0005 | 146.0 | 292 | 1.2029 | 0.8281 | |
|
| 0.0005 | 147.0 | 294 | 1.2028 | 0.8281 | |
|
| 0.0005 | 148.0 | 296 | 1.2029 | 0.8281 | |
|
| 0.0005 | 149.0 | 298 | 1.2032 | 0.8281 | |
|
| 0.0005 | 150.0 | 300 | 1.2039 | 0.8281 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.0.dev0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.13.3 |
|
|
