---
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: 1.0073
- Accuracy: 0.6875

## 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    | 0.6566          | 0.6719   |
| No log        | 2.0   | 4    | 0.6561          | 0.6719   |
| No log        | 3.0   | 6    | 0.6553          | 0.6719   |
| No log        | 4.0   | 8    | 0.6542          | 0.6719   |
| 0.4607        | 5.0   | 10   | 0.6525          | 0.6719   |
| 0.4607        | 6.0   | 12   | 0.6504          | 0.6719   |
| 0.4607        | 7.0   | 14   | 0.6480          | 0.6719   |
| 0.4607        | 8.0   | 16   | 0.6454          | 0.6719   |
| 0.4607        | 9.0   | 18   | 0.6432          | 0.6719   |
| 0.4685        | 10.0  | 20   | 0.6408          | 0.6719   |
| 0.4685        | 11.0  | 22   | 0.6383          | 0.6562   |
| 0.4685        | 12.0  | 24   | 0.6357          | 0.6719   |
| 0.4685        | 13.0  | 26   | 0.6331          | 0.6562   |
| 0.4685        | 14.0  | 28   | 0.6310          | 0.6719   |
| 0.444         | 15.0  | 30   | 0.6289          | 0.6719   |
| 0.444         | 16.0  | 32   | 0.6270          | 0.6562   |
| 0.444         | 17.0  | 34   | 0.6253          | 0.6562   |
| 0.444         | 18.0  | 36   | 0.6235          | 0.6406   |
| 0.444         | 19.0  | 38   | 0.6216          | 0.6406   |
| 0.4147        | 20.0  | 40   | 0.6199          | 0.6406   |
| 0.4147        | 21.0  | 42   | 0.6183          | 0.6562   |
| 0.4147        | 22.0  | 44   | 0.6168          | 0.6562   |
| 0.4147        | 23.0  | 46   | 0.6155          | 0.6562   |
| 0.4147        | 24.0  | 48   | 0.6144          | 0.6562   |
| 0.409         | 25.0  | 50   | 0.6133          | 0.6406   |
| 0.409         | 26.0  | 52   | 0.6123          | 0.6406   |
| 0.409         | 27.0  | 54   | 0.6116          | 0.6562   |
| 0.409         | 28.0  | 56   | 0.6110          | 0.6562   |
| 0.409         | 29.0  | 58   | 0.6103          | 0.6562   |
| 0.3614        | 30.0  | 60   | 0.6099          | 0.6719   |
| 0.3614        | 31.0  | 62   | 0.6094          | 0.6719   |
| 0.3614        | 32.0  | 64   | 0.6090          | 0.6719   |
| 0.3614        | 33.0  | 66   | 0.6090          | 0.6719   |
| 0.3614        | 34.0  | 68   | 0.6090          | 0.6875   |
| 0.3297        | 35.0  | 70   | 0.6093          | 0.6875   |
| 0.3297        | 36.0  | 72   | 0.6095          | 0.6875   |
| 0.3297        | 37.0  | 74   | 0.6093          | 0.6875   |
| 0.3297        | 38.0  | 76   | 0.6089          | 0.6875   |
| 0.3297        | 39.0  | 78   | 0.6081          | 0.6875   |
| 0.2957        | 40.0  | 80   | 0.6072          | 0.6875   |
| 0.2957        | 41.0  | 82   | 0.6064          | 0.7031   |
| 0.2957        | 42.0  | 84   | 0.6054          | 0.6875   |
| 0.2957        | 43.0  | 86   | 0.6041          | 0.6719   |
| 0.2957        | 44.0  | 88   | 0.6031          | 0.6719   |
| 0.2704        | 45.0  | 90   | 0.6022          | 0.6875   |
| 0.2704        | 46.0  | 92   | 0.6017          | 0.6875   |
| 0.2704        | 47.0  | 94   | 0.6012          | 0.6875   |
| 0.2704        | 48.0  | 96   | 0.6008          | 0.6719   |
| 0.2704        | 49.0  | 98   | 0.5998          | 0.6719   |
| 0.2302        | 50.0  | 100  | 0.5987          | 0.6875   |
| 0.2302        | 51.0  | 102  | 0.5977          | 0.6875   |
| 0.2302        | 52.0  | 104  | 0.5959          | 0.6875   |
| 0.2302        | 53.0  | 106  | 0.5935          | 0.6719   |
| 0.2302        | 54.0  | 108  | 0.5913          | 0.6719   |
| 0.198         | 55.0  | 110  | 0.5895          | 0.6719   |
| 0.198         | 56.0  | 112  | 0.5882          | 0.6719   |
| 0.198         | 57.0  | 114  | 0.5865          | 0.6875   |
| 0.198         | 58.0  | 116  | 0.5854          | 0.7031   |
| 0.198         | 59.0  | 118  | 0.5852          | 0.7031   |
| 0.1729        | 60.0  | 120  | 0.5854          | 0.7031   |
| 0.1729        | 61.0  | 122  | 0.5857          | 0.7031   |
| 0.1729        | 62.0  | 124  | 0.5861          | 0.6875   |
| 0.1729        | 63.0  | 126  | 0.5864          | 0.6875   |
| 0.1729        | 64.0  | 128  | 0.5866          | 0.6875   |
| 0.1495        | 65.0  | 130  | 0.5863          | 0.6719   |
| 0.1495        | 66.0  | 132  | 0.5858          | 0.6719   |
| 0.1495        | 67.0  | 134  | 0.5856          | 0.6875   |
| 0.1495        | 68.0  | 136  | 0.5860          | 0.6875   |
| 0.1495        | 69.0  | 138  | 0.5860          | 0.6719   |
| 0.1223        | 70.0  | 140  | 0.5863          | 0.6719   |
| 0.1223        | 71.0  | 142  | 0.5870          | 0.6875   |
| 0.1223        | 72.0  | 144  | 0.5881          | 0.6875   |
| 0.1223        | 73.0  | 146  | 0.5890          | 0.6719   |
| 0.1223        | 74.0  | 148  | 0.5899          | 0.6719   |
| 0.1034        | 75.0  | 150  | 0.5909          | 0.6719   |
| 0.1034        | 76.0  | 152  | 0.5920          | 0.6719   |
| 0.1034        | 77.0  | 154  | 0.5935          | 0.6562   |
| 0.1034        | 78.0  | 156  | 0.5953          | 0.6562   |
| 0.1034        | 79.0  | 158  | 0.5969          | 0.6562   |
| 0.087         | 80.0  | 160  | 0.5984          | 0.6562   |
| 0.087         | 81.0  | 162  | 0.6008          | 0.6562   |
| 0.087         | 82.0  | 164  | 0.6031          | 0.6719   |
| 0.087         | 83.0  | 166  | 0.6057          | 0.6562   |
| 0.087         | 84.0  | 168  | 0.6088          | 0.6562   |
| 0.0705        | 85.0  | 170  | 0.6126          | 0.6562   |
| 0.0705        | 86.0  | 172  | 0.6164          | 0.6562   |
| 0.0705        | 87.0  | 174  | 0.6198          | 0.6719   |
| 0.0705        | 88.0  | 176  | 0.6227          | 0.6719   |
| 0.0705        | 89.0  | 178  | 0.6257          | 0.6719   |
| 0.0551        | 90.0  | 180  | 0.6288          | 0.6719   |
| 0.0551        | 91.0  | 182  | 0.6321          | 0.6719   |
| 0.0551        | 92.0  | 184  | 0.6360          | 0.6719   |
| 0.0551        | 93.0  | 186  | 0.6403          | 0.6875   |
| 0.0551        | 94.0  | 188  | 0.6443          | 0.6875   |
| 0.0447        | 95.0  | 190  | 0.6489          | 0.6875   |
| 0.0447        | 96.0  | 192  | 0.6534          | 0.6875   |
| 0.0447        | 97.0  | 194  | 0.6609          | 0.7031   |
| 0.0447        | 98.0  | 196  | 0.6689          | 0.7031   |
| 0.0447        | 99.0  | 198  | 0.6772          | 0.7188   |
| 0.0345        | 100.0 | 200  | 0.6875          | 0.7188   |
| 0.0345        | 101.0 | 202  | 0.6968          | 0.7188   |
| 0.0345        | 102.0 | 204  | 0.7052          | 0.7188   |
| 0.0345        | 103.0 | 206  | 0.7126          | 0.7188   |
| 0.0345        | 104.0 | 208  | 0.7181          | 0.7188   |
| 0.0274        | 105.0 | 210  | 0.7234          | 0.7188   |
| 0.0274        | 106.0 | 212  | 0.7282          | 0.7188   |
| 0.0274        | 107.0 | 214  | 0.7361          | 0.7188   |
| 0.0274        | 108.0 | 216  | 0.7440          | 0.7031   |
| 0.0274        | 109.0 | 218  | 0.7539          | 0.7031   |
| 0.0218        | 110.0 | 220  | 0.7658          | 0.7031   |
| 0.0218        | 111.0 | 222  | 0.7772          | 0.7031   |
| 0.0218        | 112.0 | 224  | 0.7882          | 0.7031   |
| 0.0218        | 113.0 | 226  | 0.7977          | 0.7031   |
| 0.0218        | 114.0 | 228  | 0.8047          | 0.7031   |
| 0.0168        | 115.0 | 230  | 0.8103          | 0.7031   |
| 0.0168        | 116.0 | 232  | 0.8135          | 0.7031   |
| 0.0168        | 117.0 | 234  | 0.8157          | 0.7031   |
| 0.0168        | 118.0 | 236  | 0.8186          | 0.7031   |
| 0.0168        | 119.0 | 238  | 0.8213          | 0.7031   |
| 0.0138        | 120.0 | 240  | 0.8240          | 0.6875   |
| 0.0138        | 121.0 | 242  | 0.8262          | 0.6875   |
| 0.0138        | 122.0 | 244  | 0.8296          | 0.6875   |
| 0.0138        | 123.0 | 246  | 0.8339          | 0.6875   |
| 0.0138        | 124.0 | 248  | 0.8392          | 0.6875   |
| 0.0115        | 125.0 | 250  | 0.8451          | 0.6875   |
| 0.0115        | 126.0 | 252  | 0.8508          | 0.6875   |
| 0.0115        | 127.0 | 254  | 0.8574          | 0.6875   |
| 0.0115        | 128.0 | 256  | 0.8640          | 0.6875   |
| 0.0115        | 129.0 | 258  | 0.8707          | 0.6875   |
| 0.0096        | 130.0 | 260  | 0.8776          | 0.6875   |
| 0.0096        | 131.0 | 262  | 0.8845          | 0.6875   |
| 0.0096        | 132.0 | 264  | 0.8938          | 0.6875   |
| 0.0096        | 133.0 | 266  | 0.9021          | 0.6875   |
| 0.0096        | 134.0 | 268  | 0.9102          | 0.6875   |
| 0.0083        | 135.0 | 270  | 0.9181          | 0.6875   |
| 0.0083        | 136.0 | 272  | 0.9260          | 0.6875   |
| 0.0083        | 137.0 | 274  | 0.9338          | 0.6875   |
| 0.0083        | 138.0 | 276  | 0.9416          | 0.6875   |
| 0.0083        | 139.0 | 278  | 0.9488          | 0.6875   |
| 0.0071        | 140.0 | 280  | 0.9566          | 0.6875   |
| 0.0071        | 141.0 | 282  | 0.9635          | 0.6875   |
| 0.0071        | 142.0 | 284  | 0.9707          | 0.6875   |
| 0.0071        | 143.0 | 286  | 0.9769          | 0.6875   |
| 0.0071        | 144.0 | 288  | 0.9825          | 0.6875   |
| 0.0061        | 145.0 | 290  | 0.9878          | 0.6875   |
| 0.0061        | 146.0 | 292  | 0.9926          | 0.6875   |
| 0.0061        | 147.0 | 294  | 0.9962          | 0.6875   |
| 0.0061        | 148.0 | 296  | 1.0002          | 0.6875   |
| 0.0061        | 149.0 | 298  | 1.0036          | 0.6875   |
| 0.0054        | 150.0 | 300  | 1.0073          | 0.6875   |


### Framework versions

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