---
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: 1.0984
- Accuracy: 0.7969

## 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    | 0.8163          | 0.7578   |
| No log        | 2.0   | 8    | 0.8152          | 0.7578   |
| 0.3744        | 3.0   | 12   | 0.8132          | 0.7578   |
| 0.3744        | 4.0   | 16   | 0.8101          | 0.7578   |
| 0.2942        | 5.0   | 20   | 0.8062          | 0.7578   |
| 0.2942        | 6.0   | 24   | 0.8015          | 0.7578   |
| 0.2942        | 7.0   | 28   | 0.7956          | 0.7578   |
| 0.2802        | 8.0   | 32   | 0.7892          | 0.7578   |
| 0.2802        | 9.0   | 36   | 0.7832          | 0.7578   |
| 0.2775        | 10.0  | 40   | 0.7759          | 0.7656   |
| 0.2775        | 11.0  | 44   | 0.7684          | 0.7656   |
| 0.2775        | 12.0  | 48   | 0.7613          | 0.7734   |
| 0.2629        | 13.0  | 52   | 0.7535          | 0.7812   |
| 0.2629        | 14.0  | 56   | 0.7458          | 0.7812   |
| 0.1656        | 15.0  | 60   | 0.7352          | 0.7734   |
| 0.1656        | 16.0  | 64   | 0.7253          | 0.7734   |
| 0.1656        | 17.0  | 68   | 0.7179          | 0.7656   |
| 0.2032        | 18.0  | 72   | 0.7089          | 0.7734   |
| 0.2032        | 19.0  | 76   | 0.6996          | 0.7734   |
| 0.1397        | 20.0  | 80   | 0.6935          | 0.7734   |
| 0.1397        | 21.0  | 84   | 0.6916          | 0.7812   |
| 0.1397        | 22.0  | 88   | 0.6879          | 0.7891   |
| 0.0971        | 23.0  | 92   | 0.6829          | 0.7891   |
| 0.0971        | 24.0  | 96   | 0.6678          | 0.7812   |
| 0.0483        | 25.0  | 100  | 0.6503          | 0.7734   |
| 0.0483        | 26.0  | 104  | 0.6394          | 0.7891   |
| 0.0483        | 27.0  | 108  | 0.6372          | 0.7891   |
| 0.0261        | 28.0  | 112  | 0.6343          | 0.7891   |
| 0.0261        | 29.0  | 116  | 0.6357          | 0.8047   |
| 0.0186        | 30.0  | 120  | 0.6444          | 0.8047   |
| 0.0186        | 31.0  | 124  | 0.6516          | 0.8047   |
| 0.0186        | 32.0  | 128  | 0.6572          | 0.8125   |
| 0.0088        | 33.0  | 132  | 0.6569          | 0.8047   |
| 0.0088        | 34.0  | 136  | 0.6629          | 0.8047   |
| 0.0081        | 35.0  | 140  | 0.6660          | 0.8125   |
| 0.0081        | 36.0  | 144  | 0.6707          | 0.8047   |
| 0.0081        | 37.0  | 148  | 0.6774          | 0.8047   |
| 0.0056        | 38.0  | 152  | 0.6836          | 0.8047   |
| 0.0056        | 39.0  | 156  | 0.6893          | 0.8047   |
| 0.0047        | 40.0  | 160  | 0.6946          | 0.8047   |
| 0.0047        | 41.0  | 164  | 0.6988          | 0.8125   |
| 0.0047        | 42.0  | 168  | 0.7042          | 0.8203   |
| 0.0046        | 43.0  | 172  | 0.7088          | 0.8203   |
| 0.0046        | 44.0  | 176  | 0.7104          | 0.8203   |
| 0.0043        | 45.0  | 180  | 0.7139          | 0.8203   |
| 0.0043        | 46.0  | 184  | 0.7181          | 0.8203   |
| 0.0043        | 47.0  | 188  | 0.7222          | 0.8203   |
| 0.003         | 48.0  | 192  | 0.7264          | 0.8203   |
| 0.003         | 49.0  | 196  | 0.7304          | 0.8281   |
| 0.0028        | 50.0  | 200  | 0.7345          | 0.8281   |
| 0.0028        | 51.0  | 204  | 0.7391          | 0.8281   |
| 0.0028        | 52.0  | 208  | 0.7461          | 0.8203   |
| 0.0029        | 53.0  | 212  | 0.7573          | 0.8281   |
| 0.0029        | 54.0  | 216  | 0.7679          | 0.8125   |
| 0.0025        | 55.0  | 220  | 0.7920          | 0.8125   |
| 0.0025        | 56.0  | 224  | 0.8067          | 0.8125   |
| 0.0025        | 57.0  | 228  | 0.8099          | 0.8125   |
| 0.0021        | 58.0  | 232  | 0.8078          | 0.8203   |
| 0.0021        | 59.0  | 236  | 0.8036          | 0.8203   |
| 0.0018        | 60.0  | 240  | 0.8014          | 0.8125   |
| 0.0018        | 61.0  | 244  | 0.8013          | 0.8203   |
| 0.0018        | 62.0  | 248  | 0.8025          | 0.8281   |
| 0.0017        | 63.0  | 252  | 0.8049          | 0.8281   |
| 0.0017        | 64.0  | 256  | 0.8072          | 0.8281   |
| 0.0015        | 65.0  | 260  | 0.8103          | 0.8203   |
| 0.0015        | 66.0  | 264  | 0.8147          | 0.8203   |
| 0.0015        | 67.0  | 268  | 0.8187          | 0.8203   |
| 0.0014        | 68.0  | 272  | 0.8226          | 0.8281   |
| 0.0014        | 69.0  | 276  | 0.8266          | 0.8281   |
| 0.0013        | 70.0  | 280  | 0.8308          | 0.8281   |
| 0.0013        | 71.0  | 284  | 0.8349          | 0.8281   |
| 0.0013        | 72.0  | 288  | 0.8390          | 0.8281   |
| 0.0012        | 73.0  | 292  | 0.8431          | 0.8281   |
| 0.0012        | 74.0  | 296  | 0.8472          | 0.8281   |
| 0.0011        | 75.0  | 300  | 0.8513          | 0.8281   |
| 0.0011        | 76.0  | 304  | 0.8554          | 0.8281   |
| 0.0011        | 77.0  | 308  | 0.8595          | 0.8281   |
| 0.001         | 78.0  | 312  | 0.8637          | 0.8281   |
| 0.001         | 79.0  | 316  | 0.8678          | 0.8281   |
| 0.0009        | 80.0  | 320  | 0.8720          | 0.8281   |
| 0.0009        | 81.0  | 324  | 0.8762          | 0.8281   |
| 0.0009        | 82.0  | 328  | 0.8805          | 0.8203   |
| 0.0009        | 83.0  | 332  | 0.8847          | 0.8203   |
| 0.0009        | 84.0  | 336  | 0.8889          | 0.8203   |
| 0.0008        | 85.0  | 340  | 0.8931          | 0.8203   |
| 0.0008        | 86.0  | 344  | 0.8976          | 0.8281   |
| 0.0008        | 87.0  | 348  | 0.9021          | 0.8281   |
| 0.0007        | 88.0  | 352  | 0.9063          | 0.8281   |
| 0.0007        | 89.0  | 356  | 0.9102          | 0.8281   |
| 0.0007        | 90.0  | 360  | 0.9137          | 0.8281   |
| 0.0007        | 91.0  | 364  | 0.9173          | 0.8281   |
| 0.0007        | 92.0  | 368  | 0.9208          | 0.8203   |
| 0.0006        | 93.0  | 372  | 0.9244          | 0.8125   |
| 0.0006        | 94.0  | 376  | 0.9279          | 0.8125   |
| 0.0006        | 95.0  | 380  | 0.9315          | 0.8125   |
| 0.0006        | 96.0  | 384  | 0.9351          | 0.8125   |
| 0.0006        | 97.0  | 388  | 0.9389          | 0.8125   |
| 0.0006        | 98.0  | 392  | 0.9442          | 0.8281   |
| 0.0006        | 99.0  | 396  | 0.9564          | 0.8438   |
| 0.0005        | 100.0 | 400  | 0.9671          | 0.8359   |
| 0.0005        | 101.0 | 404  | 0.9756          | 0.8281   |
| 0.0005        | 102.0 | 408  | 0.9801          | 0.8281   |
| 0.0005        | 103.0 | 412  | 0.9794          | 0.8438   |
| 0.0005        | 104.0 | 416  | 0.9766          | 0.8438   |
| 0.0005        | 105.0 | 420  | 0.9766          | 0.8438   |
| 0.0005        | 106.0 | 424  | 0.9782          | 0.8359   |
| 0.0005        | 107.0 | 428  | 0.9808          | 0.8359   |
| 0.0004        | 108.0 | 432  | 0.9840          | 0.8359   |
| 0.0004        | 109.0 | 436  | 0.9877          | 0.8359   |
| 0.0004        | 110.0 | 440  | 0.9920          | 0.8047   |
| 0.0004        | 111.0 | 444  | 0.9988          | 0.7969   |
| 0.0004        | 112.0 | 448  | 1.0040          | 0.8047   |
| 0.0004        | 113.0 | 452  | 1.0075          | 0.8047   |
| 0.0004        | 114.0 | 456  | 1.0109          | 0.8047   |
| 0.0003        | 115.0 | 460  | 1.0142          | 0.8047   |
| 0.0003        | 116.0 | 464  | 1.0175          | 0.8047   |
| 0.0003        | 117.0 | 468  | 1.0209          | 0.8047   |
| 0.0003        | 118.0 | 472  | 1.0244          | 0.8047   |
| 0.0003        | 119.0 | 476  | 1.0279          | 0.8047   |
| 0.0003        | 120.0 | 480  | 1.0317          | 0.8047   |
| 0.0003        | 121.0 | 484  | 1.0357          | 0.8047   |
| 0.0003        | 122.0 | 488  | 1.0398          | 0.8047   |
| 0.0003        | 123.0 | 492  | 1.0436          | 0.8047   |
| 0.0003        | 124.0 | 496  | 1.0476          | 0.7969   |
| 0.0003        | 125.0 | 500  | 1.0514          | 0.7969   |
| 0.0003        | 126.0 | 504  | 1.0552          | 0.7969   |
| 0.0003        | 127.0 | 508  | 1.0588          | 0.7969   |
| 0.0003        | 128.0 | 512  | 1.0619          | 0.7969   |
| 0.0003        | 129.0 | 516  | 1.0648          | 0.7969   |
| 0.0002        | 130.0 | 520  | 1.0676          | 0.7969   |
| 0.0002        | 131.0 | 524  | 1.0693          | 0.8047   |
| 0.0002        | 132.0 | 528  | 1.0721          | 0.7969   |
| 0.0003        | 133.0 | 532  | 1.0758          | 0.8047   |
| 0.0003        | 134.0 | 536  | 1.0791          | 0.8047   |
| 0.0002        | 135.0 | 540  | 1.0807          | 0.7969   |
| 0.0002        | 136.0 | 544  | 1.0828          | 0.8047   |
| 0.0002        | 137.0 | 548  | 1.0853          | 0.8047   |
| 0.0002        | 138.0 | 552  | 1.0879          | 0.8047   |
| 0.0002        | 139.0 | 556  | 1.0899          | 0.8047   |
| 0.0002        | 140.0 | 560  | 1.0916          | 0.8047   |
| 0.0002        | 141.0 | 564  | 1.0930          | 0.8047   |
| 0.0002        | 142.0 | 568  | 1.0941          | 0.8047   |
| 0.0002        | 143.0 | 572  | 1.0951          | 0.7969   |
| 0.0002        | 144.0 | 576  | 1.0960          | 0.7969   |
| 0.0002        | 145.0 | 580  | 1.0967          | 0.7969   |
| 0.0002        | 146.0 | 584  | 1.0973          | 0.7969   |
| 0.0002        | 147.0 | 588  | 1.0978          | 0.7969   |
| 0.0002        | 148.0 | 592  | 1.0981          | 0.7969   |
| 0.0002        | 149.0 | 596  | 1.0983          | 0.7969   |
| 0.0002        | 150.0 | 600  | 1.0984          | 0.7969   |


### Framework versions

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