---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-sst-2-64-13
  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-2-64-13

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: 1.3339
- 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   | 4    | 0.9854          | 0.8594   |
| No log        | 2.0   | 8    | 0.9825          | 0.8594   |
| 0.3525        | 3.0   | 12   | 0.9791          | 0.8672   |
| 0.3525        | 4.0   | 16   | 0.9752          | 0.8672   |
| 0.2499        | 5.0   | 20   | 0.9700          | 0.8672   |
| 0.2499        | 6.0   | 24   | 0.9629          | 0.8594   |
| 0.2499        | 7.0   | 28   | 0.9588          | 0.8594   |
| 0.2671        | 8.0   | 32   | 0.9589          | 0.8516   |
| 0.2671        | 9.0   | 36   | 0.9573          | 0.8516   |
| 0.1578        | 10.0  | 40   | 0.9487          | 0.8516   |
| 0.1578        | 11.0  | 44   | 0.9428          | 0.8516   |
| 0.1578        | 12.0  | 48   | 0.9323          | 0.8516   |
| 0.1695        | 13.0  | 52   | 0.9192          | 0.8594   |
| 0.1695        | 14.0  | 56   | 0.9121          | 0.8516   |
| 0.106         | 15.0  | 60   | 0.9055          | 0.8516   |
| 0.106         | 16.0  | 64   | 0.8947          | 0.8594   |
| 0.106         | 17.0  | 68   | 0.8886          | 0.875    |
| 0.1075        | 18.0  | 72   | 0.8914          | 0.8672   |
| 0.1075        | 19.0  | 76   | 0.8882          | 0.8672   |
| 0.0226        | 20.0  | 80   | 0.8871          | 0.8672   |
| 0.0226        | 21.0  | 84   | 0.8825          | 0.8594   |
| 0.0226        | 22.0  | 88   | 0.8841          | 0.8594   |
| 0.0045        | 23.0  | 92   | 0.8858          | 0.8672   |
| 0.0045        | 24.0  | 96   | 0.8902          | 0.875    |
| 0.0108        | 25.0  | 100  | 0.8941          | 0.8672   |
| 0.0108        | 26.0  | 104  | 0.8965          | 0.8672   |
| 0.0108        | 27.0  | 108  | 0.9028          | 0.8594   |
| 0.0242        | 28.0  | 112  | 0.9052          | 0.8594   |
| 0.0242        | 29.0  | 116  | 0.9104          | 0.8594   |
| 0.0004        | 30.0  | 120  | 0.9156          | 0.8594   |
| 0.0004        | 31.0  | 124  | 0.9166          | 0.8594   |
| 0.0004        | 32.0  | 128  | 0.9117          | 0.8594   |
| 0.0004        | 33.0  | 132  | 0.9111          | 0.8594   |
| 0.0004        | 34.0  | 136  | 0.9245          | 0.875    |
| 0.0011        | 35.0  | 140  | 0.9451          | 0.8594   |
| 0.0011        | 36.0  | 144  | 0.9664          | 0.8516   |
| 0.0011        | 37.0  | 148  | 0.9794          | 0.8359   |
| 0.0002        | 38.0  | 152  | 0.9838          | 0.8359   |
| 0.0002        | 39.0  | 156  | 0.9680          | 0.8594   |
| 0.0003        | 40.0  | 160  | 0.9540          | 0.8516   |
| 0.0003        | 41.0  | 164  | 0.9479          | 0.8672   |
| 0.0003        | 42.0  | 168  | 0.9734          | 0.8516   |
| 0.0003        | 43.0  | 172  | 0.9954          | 0.8516   |
| 0.0003        | 44.0  | 176  | 1.0139          | 0.8594   |
| 0.0002        | 45.0  | 180  | 1.0285          | 0.8516   |
| 0.0002        | 46.0  | 184  | 1.0383          | 0.8359   |
| 0.0002        | 47.0  | 188  | 1.0443          | 0.8359   |
| 0.0002        | 48.0  | 192  | 1.0474          | 0.8359   |
| 0.0002        | 49.0  | 196  | 1.0490          | 0.8359   |
| 0.0004        | 50.0  | 200  | 1.0141          | 0.8516   |
| 0.0004        | 51.0  | 204  | 0.9861          | 0.8672   |
| 0.0004        | 52.0  | 208  | 0.9913          | 0.8672   |
| 0.0204        | 53.0  | 212  | 1.0418          | 0.8594   |
| 0.0204        | 54.0  | 216  | 1.0818          | 0.8438   |
| 0.0002        | 55.0  | 220  | 1.1084          | 0.8359   |
| 0.0002        | 56.0  | 224  | 1.1198          | 0.8438   |
| 0.0002        | 57.0  | 228  | 1.1048          | 0.8359   |
| 0.0002        | 58.0  | 232  | 1.0871          | 0.8516   |
| 0.0002        | 59.0  | 236  | 1.0756          | 0.8516   |
| 0.0002        | 60.0  | 240  | 1.0676          | 0.8516   |
| 0.0002        | 61.0  | 244  | 1.0631          | 0.8516   |
| 0.0002        | 62.0  | 248  | 1.0605          | 0.8516   |
| 0.0001        | 63.0  | 252  | 1.0594          | 0.8594   |
| 0.0001        | 64.0  | 256  | 1.0592          | 0.8516   |
| 0.0001        | 65.0  | 260  | 1.0597          | 0.8594   |
| 0.0001        | 66.0  | 264  | 1.0594          | 0.8594   |
| 0.0001        | 67.0  | 268  | 1.0597          | 0.8516   |
| 0.0001        | 68.0  | 272  | 1.0606          | 0.8516   |
| 0.0001        | 69.0  | 276  | 1.0794          | 0.8516   |
| 0.0003        | 70.0  | 280  | 1.1418          | 0.8438   |
| 0.0003        | 71.0  | 284  | 1.1868          | 0.8516   |
| 0.0003        | 72.0  | 288  | 1.2120          | 0.8516   |
| 0.0001        | 73.0  | 292  | 1.2064          | 0.8516   |
| 0.0001        | 74.0  | 296  | 1.1566          | 0.8438   |
| 0.0002        | 75.0  | 300  | 1.1006          | 0.8516   |
| 0.0002        | 76.0  | 304  | 1.0705          | 0.8516   |
| 0.0002        | 77.0  | 308  | 1.0654          | 0.8516   |
| 0.0001        | 78.0  | 312  | 1.0651          | 0.8594   |
| 0.0001        | 79.0  | 316  | 1.0659          | 0.8594   |
| 0.0001        | 80.0  | 320  | 1.0674          | 0.8516   |
| 0.0001        | 81.0  | 324  | 1.0691          | 0.8516   |
| 0.0001        | 82.0  | 328  | 1.0786          | 0.8516   |
| 0.0001        | 83.0  | 332  | 1.0875          | 0.8516   |
| 0.0001        | 84.0  | 336  | 1.0948          | 0.8438   |
| 0.0001        | 85.0  | 340  | 1.1004          | 0.8438   |
| 0.0001        | 86.0  | 344  | 1.1058          | 0.8438   |
| 0.0001        | 87.0  | 348  | 1.1103          | 0.8438   |
| 0.0001        | 88.0  | 352  | 1.1136          | 0.8438   |
| 0.0001        | 89.0  | 356  | 1.1162          | 0.8438   |
| 0.0001        | 90.0  | 360  | 1.1180          | 0.8438   |
| 0.0001        | 91.0  | 364  | 1.1119          | 0.8438   |
| 0.0001        | 92.0  | 368  | 1.1084          | 0.8438   |
| 0.0001        | 93.0  | 372  | 1.1066          | 0.8516   |
| 0.0001        | 94.0  | 376  | 1.1059          | 0.8516   |
| 0.0001        | 95.0  | 380  | 1.1059          | 0.8516   |
| 0.0001        | 96.0  | 384  | 1.1065          | 0.8516   |
| 0.0001        | 97.0  | 388  | 1.1084          | 0.8516   |
| 0.0064        | 98.0  | 392  | 1.1955          | 0.8438   |
| 0.0064        | 99.0  | 396  | 1.2544          | 0.8516   |
| 0.0001        | 100.0 | 400  | 1.3053          | 0.8359   |
| 0.0001        | 101.0 | 404  | 1.3606          | 0.8281   |
| 0.0001        | 102.0 | 408  | 1.3399          | 0.8281   |
| 0.0068        | 103.0 | 412  | 1.2648          | 0.8516   |
| 0.0068        | 104.0 | 416  | 1.1161          | 0.8516   |
| 0.0001        | 105.0 | 420  | 1.0830          | 0.8594   |
| 0.0001        | 106.0 | 424  | 1.1095          | 0.8672   |
| 0.0001        | 107.0 | 428  | 1.0817          | 0.8672   |
| 0.0139        | 108.0 | 432  | 1.1057          | 0.8516   |
| 0.0139        | 109.0 | 436  | 1.1392          | 0.8438   |
| 0.0001        | 110.0 | 440  | 1.1623          | 0.8438   |
| 0.0001        | 111.0 | 444  | 1.1707          | 0.8438   |
| 0.0001        | 112.0 | 448  | 1.1766          | 0.8438   |
| 0.0001        | 113.0 | 452  | 1.1808          | 0.8516   |
| 0.0001        | 114.0 | 456  | 1.1826          | 0.8516   |
| 0.0001        | 115.0 | 460  | 1.1809          | 0.8438   |
| 0.0001        | 116.0 | 464  | 1.1380          | 0.8438   |
| 0.0001        | 117.0 | 468  | 1.1289          | 0.8594   |
| 0.0001        | 118.0 | 472  | 1.1853          | 0.8594   |
| 0.0001        | 119.0 | 476  | 1.2030          | 0.8594   |
| 0.0001        | 120.0 | 480  | 1.1913          | 0.8594   |
| 0.0001        | 121.0 | 484  | 1.1660          | 0.8672   |
| 0.0001        | 122.0 | 488  | 1.1591          | 0.8594   |
| 0.0001        | 123.0 | 492  | 1.1678          | 0.8438   |
| 0.0001        | 124.0 | 496  | 1.1800          | 0.8516   |
| 0.0001        | 125.0 | 500  | 1.1896          | 0.8516   |
| 0.0001        | 126.0 | 504  | 1.1972          | 0.8516   |
| 0.0001        | 127.0 | 508  | 1.2034          | 0.8516   |
| 0.0001        | 128.0 | 512  | 1.2074          | 0.8438   |
| 0.0001        | 129.0 | 516  | 1.2104          | 0.8438   |
| 0.0           | 130.0 | 520  | 1.2126          | 0.8438   |
| 0.0           | 131.0 | 524  | 1.1920          | 0.8672   |
| 0.0           | 132.0 | 528  | 1.2214          | 0.8516   |
| 0.0007        | 133.0 | 532  | 1.2321          | 0.8516   |
| 0.0007        | 134.0 | 536  | 1.2382          | 0.8516   |
| 0.0001        | 135.0 | 540  | 1.2297          | 0.8516   |
| 0.0001        | 136.0 | 544  | 1.1786          | 0.8516   |
| 0.0001        | 137.0 | 548  | 1.2126          | 0.8516   |
| 0.0001        | 138.0 | 552  | 1.2706          | 0.8516   |
| 0.0001        | 139.0 | 556  | 1.2978          | 0.8516   |
| 0.0           | 140.0 | 560  | 1.3119          | 0.8516   |
| 0.0           | 141.0 | 564  | 1.3222          | 0.8438   |
| 0.0           | 142.0 | 568  | 1.3290          | 0.8438   |
| 0.0           | 143.0 | 572  | 1.3333          | 0.8438   |
| 0.0           | 144.0 | 576  | 1.3357          | 0.8438   |
| 0.0           | 145.0 | 580  | 1.3371          | 0.8438   |
| 0.0           | 146.0 | 584  | 1.3371          | 0.8438   |
| 0.0           | 147.0 | 588  | 1.3353          | 0.8438   |
| 0.0001        | 148.0 | 592  | 1.3344          | 0.8438   |
| 0.0001        | 149.0 | 596  | 1.3340          | 0.8438   |
| 0.0           | 150.0 | 600  | 1.3339          | 0.8438   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3