---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-sst-2-64-21
  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-2-64-21

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: 1.0374
- Accuracy: 0.8906

## 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    | 1.1068          | 0.8672   |
| No log        | 2.0   | 8    | 1.1055          | 0.8672   |
| 0.5789        | 3.0   | 12   | 1.1002          | 0.8672   |
| 0.5789        | 4.0   | 16   | 1.0902          | 0.8672   |
| 0.4952        | 5.0   | 20   | 1.0797          | 0.8672   |
| 0.4952        | 6.0   | 24   | 1.0662          | 0.8672   |
| 0.4952        | 7.0   | 28   | 1.0461          | 0.8672   |
| 0.4202        | 8.0   | 32   | 1.0329          | 0.8672   |
| 0.4202        | 9.0   | 36   | 1.0326          | 0.8672   |
| 0.5159        | 10.0  | 40   | 1.0217          | 0.8672   |
| 0.5159        | 11.0  | 44   | 1.0053          | 0.8672   |
| 0.5159        | 12.0  | 48   | 0.9908          | 0.875    |
| 0.4018        | 13.0  | 52   | 0.9818          | 0.8828   |
| 0.4018        | 14.0  | 56   | 0.9686          | 0.8828   |
| 0.2452        | 15.0  | 60   | 0.9591          | 0.8828   |
| 0.2452        | 16.0  | 64   | 0.9489          | 0.8828   |
| 0.2452        | 17.0  | 68   | 0.9421          | 0.8828   |
| 0.1966        | 18.0  | 72   | 0.9354          | 0.8828   |
| 0.1966        | 19.0  | 76   | 0.9318          | 0.8906   |
| 0.1955        | 20.0  | 80   | 0.9353          | 0.8828   |
| 0.1955        | 21.0  | 84   | 0.9552          | 0.8828   |
| 0.1955        | 22.0  | 88   | 0.9728          | 0.875    |
| 0.1316        | 23.0  | 92   | 0.9686          | 0.875    |
| 0.1316        | 24.0  | 96   | 0.9555          | 0.875    |
| 0.0488        | 25.0  | 100  | 0.9442          | 0.8828   |
| 0.0488        | 26.0  | 104  | 0.9410          | 0.8828   |
| 0.0488        | 27.0  | 108  | 0.9413          | 0.8828   |
| 0.0023        | 28.0  | 112  | 0.9522          | 0.8828   |
| 0.0023        | 29.0  | 116  | 0.9614          | 0.8828   |
| 0.0019        | 30.0  | 120  | 0.9603          | 0.8828   |
| 0.0019        | 31.0  | 124  | 0.9474          | 0.8828   |
| 0.0019        | 32.0  | 128  | 0.9408          | 0.8906   |
| 0.0136        | 33.0  | 132  | 0.9417          | 0.8906   |
| 0.0136        | 34.0  | 136  | 0.9433          | 0.8906   |
| 0.0037        | 35.0  | 140  | 0.9412          | 0.8906   |
| 0.0037        | 36.0  | 144  | 0.9529          | 0.8906   |
| 0.0037        | 37.0  | 148  | 0.9641          | 0.8828   |
| 0.0003        | 38.0  | 152  | 0.9868          | 0.8828   |
| 0.0003        | 39.0  | 156  | 0.9985          | 0.875    |
| 0.0002        | 40.0  | 160  | 1.0006          | 0.875    |
| 0.0002        | 41.0  | 164  | 1.0009          | 0.875    |
| 0.0002        | 42.0  | 168  | 1.0038          | 0.875    |
| 0.0013        | 43.0  | 172  | 0.9982          | 0.8828   |
| 0.0013        | 44.0  | 176  | 0.9853          | 0.8828   |
| 0.0102        | 45.0  | 180  | 0.9790          | 0.8828   |
| 0.0102        | 46.0  | 184  | 0.9900          | 0.8828   |
| 0.0102        | 47.0  | 188  | 1.0004          | 0.8828   |
| 0.0002        | 48.0  | 192  | 1.0063          | 0.875    |
| 0.0002        | 49.0  | 196  | 1.0095          | 0.875    |
| 0.0001        | 50.0  | 200  | 1.0136          | 0.875    |
| 0.0001        | 51.0  | 204  | 1.0180          | 0.8672   |
| 0.0001        | 52.0  | 208  | 1.0206          | 0.8672   |
| 0.0001        | 53.0  | 212  | 1.0178          | 0.8672   |
| 0.0001        | 54.0  | 216  | 1.0157          | 0.8672   |
| 0.0001        | 55.0  | 220  | 1.0140          | 0.875    |
| 0.0001        | 56.0  | 224  | 1.0128          | 0.875    |
| 0.0001        | 57.0  | 228  | 1.0117          | 0.875    |
| 0.0001        | 58.0  | 232  | 1.0097          | 0.875    |
| 0.0001        | 59.0  | 236  | 1.0082          | 0.875    |
| 0.0001        | 60.0  | 240  | 1.0002          | 0.8828   |
| 0.0001        | 61.0  | 244  | 0.9944          | 0.8828   |
| 0.0001        | 62.0  | 248  | 0.9913          | 0.8906   |
| 0.0001        | 63.0  | 252  | 0.9897          | 0.8906   |
| 0.0001        | 64.0  | 256  | 0.9893          | 0.8906   |
| 0.0001        | 65.0  | 260  | 0.9895          | 0.8906   |
| 0.0001        | 66.0  | 264  | 0.9899          | 0.8906   |
| 0.0001        | 67.0  | 268  | 0.9905          | 0.8906   |
| 0.0001        | 68.0  | 272  | 0.9913          | 0.8906   |
| 0.0001        | 69.0  | 276  | 0.9962          | 0.8906   |
| 0.0001        | 70.0  | 280  | 1.0023          | 0.8828   |
| 0.0001        | 71.0  | 284  | 1.0079          | 0.8828   |
| 0.0001        | 72.0  | 288  | 1.0118          | 0.875    |
| 0.0001        | 73.0  | 292  | 1.0144          | 0.875    |
| 0.0001        | 74.0  | 296  | 1.0161          | 0.875    |
| 0.0001        | 75.0  | 300  | 1.0172          | 0.875    |
| 0.0001        | 76.0  | 304  | 1.0178          | 0.875    |
| 0.0001        | 77.0  | 308  | 1.0241          | 0.875    |
| 0.0183        | 78.0  | 312  | 1.0549          | 0.8672   |
| 0.0183        | 79.0  | 316  | 1.0631          | 0.8672   |
| 0.0001        | 80.0  | 320  | 1.0629          | 0.875    |
| 0.0001        | 81.0  | 324  | 1.0650          | 0.875    |
| 0.0001        | 82.0  | 328  | 1.0672          | 0.8594   |
| 0.0001        | 83.0  | 332  | 1.0686          | 0.8594   |
| 0.0001        | 84.0  | 336  | 1.0632          | 0.875    |
| 0.0131        | 85.0  | 340  | 1.0157          | 0.8672   |
| 0.0131        | 86.0  | 344  | 0.9959          | 0.8828   |
| 0.0131        | 87.0  | 348  | 0.9946          | 0.8906   |
| 0.0001        | 88.0  | 352  | 0.9933          | 0.8906   |
| 0.0001        | 89.0  | 356  | 0.9933          | 0.8906   |
| 0.0001        | 90.0  | 360  | 0.9941          | 0.8828   |
| 0.0001        | 91.0  | 364  | 0.9949          | 0.8828   |
| 0.0001        | 92.0  | 368  | 0.9954          | 0.8828   |
| 0.0001        | 93.0  | 372  | 0.9959          | 0.8828   |
| 0.0001        | 94.0  | 376  | 0.9962          | 0.8828   |
| 0.0001        | 95.0  | 380  | 0.9961          | 0.8828   |
| 0.0001        | 96.0  | 384  | 0.9963          | 0.8828   |
| 0.0001        | 97.0  | 388  | 0.9967          | 0.8828   |
| 0.0001        | 98.0  | 392  | 0.9987          | 0.8906   |
| 0.0001        | 99.0  | 396  | 1.0214          | 0.8828   |
| 0.0105        | 100.0 | 400  | 1.0346          | 0.875    |
| 0.0105        | 101.0 | 404  | 1.0406          | 0.875    |
| 0.0105        | 102.0 | 408  | 1.0435          | 0.875    |
| 0.0001        | 103.0 | 412  | 1.0444          | 0.875    |
| 0.0001        | 104.0 | 416  | 1.0446          | 0.875    |
| 0.0001        | 105.0 | 420  | 1.0447          | 0.875    |
| 0.0001        | 106.0 | 424  | 1.0448          | 0.875    |
| 0.0001        | 107.0 | 428  | 1.0453          | 0.8828   |
| 0.0001        | 108.0 | 432  | 1.0457          | 0.8828   |
| 0.0001        | 109.0 | 436  | 1.0488          | 0.875    |
| 0.0184        | 110.0 | 440  | 1.0597          | 0.875    |
| 0.0184        | 111.0 | 444  | 1.0939          | 0.8594   |
| 0.0184        | 112.0 | 448  | 1.1410          | 0.8438   |
| 0.0001        | 113.0 | 452  | 1.1659          | 0.8438   |
| 0.0001        | 114.0 | 456  | 1.1104          | 0.8594   |
| 0.0001        | 115.0 | 460  | 1.0816          | 0.8672   |
| 0.0001        | 116.0 | 464  | 1.0695          | 0.875    |
| 0.0001        | 117.0 | 468  | 1.0702          | 0.875    |
| 0.0           | 118.0 | 472  | 1.0709          | 0.875    |
| 0.0           | 119.0 | 476  | 1.0704          | 0.875    |
| 0.0           | 120.0 | 480  | 1.0693          | 0.875    |
| 0.0           | 121.0 | 484  | 1.0684          | 0.875    |
| 0.0           | 122.0 | 488  | 1.0677          | 0.875    |
| 0.0           | 123.0 | 492  | 1.0676          | 0.875    |
| 0.0           | 124.0 | 496  | 1.0676          | 0.875    |
| 0.0           | 125.0 | 500  | 1.0675          | 0.875    |
| 0.0           | 126.0 | 504  | 1.0675          | 0.875    |
| 0.0           | 127.0 | 508  | 1.0676          | 0.875    |
| 0.0           | 128.0 | 512  | 1.0687          | 0.875    |
| 0.0           | 129.0 | 516  | 1.0694          | 0.875    |
| 0.0           | 130.0 | 520  | 1.0701          | 0.875    |
| 0.0           | 131.0 | 524  | 1.0707          | 0.875    |
| 0.0           | 132.0 | 528  | 1.0661          | 0.875    |
| 0.0001        | 133.0 | 532  | 1.0391          | 0.8906   |
| 0.0001        | 134.0 | 536  | 1.0258          | 0.8906   |
| 0.0           | 135.0 | 540  | 1.0188          | 0.8906   |
| 0.0           | 136.0 | 544  | 1.0171          | 0.8906   |
| 0.0           | 137.0 | 548  | 1.0188          | 0.8828   |
| 0.0           | 138.0 | 552  | 1.0210          | 0.875    |
| 0.0           | 139.0 | 556  | 1.0223          | 0.875    |
| 0.0001        | 140.0 | 560  | 1.0202          | 0.8828   |
| 0.0001        | 141.0 | 564  | 1.0235          | 0.8906   |
| 0.0001        | 142.0 | 568  | 1.0288          | 0.8906   |
| 0.0           | 143.0 | 572  | 1.0322          | 0.8906   |
| 0.0           | 144.0 | 576  | 1.0343          | 0.8906   |
| 0.0           | 145.0 | 580  | 1.0356          | 0.8906   |
| 0.0           | 146.0 | 584  | 1.0364          | 0.8906   |
| 0.0           | 147.0 | 588  | 1.0369          | 0.8906   |
| 0.0           | 148.0 | 592  | 1.0372          | 0.8906   |
| 0.0           | 149.0 | 596  | 1.0374          | 0.8906   |
| 0.0           | 150.0 | 600  | 1.0374          | 0.8906   |


### Framework versions

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