---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-87
  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-yelp_polarity-64-87

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6151
- Accuracy: 0.9453

## 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    | 0.3505          | 0.9531   |
| No log        | 2.0   | 8    | 0.3554          | 0.9531   |
| 0.7909        | 3.0   | 12   | 0.3781          | 0.9531   |
| 0.7909        | 4.0   | 16   | 0.4031          | 0.9531   |
| 0.5682        | 5.0   | 20   | 0.4409          | 0.9375   |
| 0.5682        | 6.0   | 24   | 0.5003          | 0.9453   |
| 0.5682        | 7.0   | 28   | 0.5068          | 0.9453   |
| 0.6452        | 8.0   | 32   | 0.4511          | 0.9453   |
| 0.6452        | 9.0   | 36   | 0.3963          | 0.9531   |
| 0.5947        | 10.0  | 40   | 0.3820          | 0.9531   |
| 0.5947        | 11.0  | 44   | 0.3797          | 0.9453   |
| 0.5947        | 12.0  | 48   | 0.4010          | 0.9453   |
| 0.6099        | 13.0  | 52   | 0.3783          | 0.9531   |
| 0.6099        | 14.0  | 56   | 0.3875          | 0.9531   |
| 0.3653        | 15.0  | 60   | 0.3945          | 0.9453   |
| 0.3653        | 16.0  | 64   | 0.4100          | 0.9453   |
| 0.3653        | 17.0  | 68   | 0.4161          | 0.9531   |
| 0.3425        | 18.0  | 72   | 0.4085          | 0.9531   |
| 0.3425        | 19.0  | 76   | 0.3950          | 0.9453   |
| 0.3247        | 20.0  | 80   | 0.3743          | 0.9531   |
| 0.3247        | 21.0  | 84   | 0.4230          | 0.9531   |
| 0.3247        | 22.0  | 88   | 0.4502          | 0.9453   |
| 0.2242        | 23.0  | 92   | 0.3965          | 0.9531   |
| 0.2242        | 24.0  | 96   | 0.3779          | 0.9453   |
| 0.1052        | 25.0  | 100  | 0.3940          | 0.9297   |
| 0.1052        | 26.0  | 104  | 0.4213          | 0.9375   |
| 0.1052        | 27.0  | 108  | 0.4330          | 0.9297   |
| 0.018         | 28.0  | 112  | 0.4165          | 0.9453   |
| 0.018         | 29.0  | 116  | 0.4136          | 0.9453   |
| 0.002         | 30.0  | 120  | 0.4521          | 0.9219   |
| 0.002         | 31.0  | 124  | 0.4985          | 0.9141   |
| 0.002         | 32.0  | 128  | 0.5143          | 0.9141   |
| 0.0002        | 33.0  | 132  | 0.5213          | 0.9141   |
| 0.0002        | 34.0  | 136  | 0.4808          | 0.9219   |
| 0.0002        | 35.0  | 140  | 0.4556          | 0.9453   |
| 0.0002        | 36.0  | 144  | 0.4534          | 0.9453   |
| 0.0002        | 37.0  | 148  | 0.4546          | 0.9453   |
| 0.0001        | 38.0  | 152  | 0.4599          | 0.9453   |
| 0.0001        | 39.0  | 156  | 0.4673          | 0.9453   |
| 0.0001        | 40.0  | 160  | 0.4749          | 0.9453   |
| 0.0001        | 41.0  | 164  | 0.4821          | 0.9453   |
| 0.0001        | 42.0  | 168  | 0.4891          | 0.9453   |
| 0.0001        | 43.0  | 172  | 0.4956          | 0.9375   |
| 0.0001        | 44.0  | 176  | 0.4995          | 0.9453   |
| 0.0001        | 45.0  | 180  | 0.5077          | 0.9375   |
| 0.0001        | 46.0  | 184  | 0.5162          | 0.9375   |
| 0.0001        | 47.0  | 188  | 0.5253          | 0.9375   |
| 0.0           | 48.0  | 192  | 0.5321          | 0.9375   |
| 0.0           | 49.0  | 196  | 0.5369          | 0.9375   |
| 0.0001        | 50.0  | 200  | 0.5388          | 0.9375   |
| 0.0001        | 51.0  | 204  | 0.5248          | 0.9453   |
| 0.0001        | 52.0  | 208  | 0.5274          | 0.9375   |
| 0.0001        | 53.0  | 212  | 0.5331          | 0.9297   |
| 0.0001        | 54.0  | 216  | 0.5374          | 0.9297   |
| 0.0           | 55.0  | 220  | 0.5403          | 0.9297   |
| 0.0           | 56.0  | 224  | 0.5447          | 0.9297   |
| 0.0           | 57.0  | 228  | 0.5478          | 0.9297   |
| 0.0           | 58.0  | 232  | 0.5497          | 0.9297   |
| 0.0           | 59.0  | 236  | 0.5505          | 0.9297   |
| 0.0           | 60.0  | 240  | 0.5511          | 0.9297   |
| 0.0           | 61.0  | 244  | 0.5518          | 0.9375   |
| 0.0           | 62.0  | 248  | 0.5498          | 0.9375   |
| 0.0           | 63.0  | 252  | 0.5507          | 0.9453   |
| 0.0           | 64.0  | 256  | 0.5542          | 0.9453   |
| 0.0           | 65.0  | 260  | 0.5578          | 0.9453   |
| 0.0           | 66.0  | 264  | 0.5610          | 0.9453   |
| 0.0           | 67.0  | 268  | 0.5637          | 0.9453   |
| 0.0           | 68.0  | 272  | 0.5662          | 0.9453   |
| 0.0           | 69.0  | 276  | 0.5685          | 0.9453   |
| 0.0           | 70.0  | 280  | 0.5705          | 0.9453   |
| 0.0           | 71.0  | 284  | 0.5725          | 0.9453   |
| 0.0           | 72.0  | 288  | 0.5738          | 0.9453   |
| 0.0           | 73.0  | 292  | 0.5753          | 0.9453   |
| 0.0           | 74.0  | 296  | 0.5768          | 0.9453   |
| 0.0           | 75.0  | 300  | 0.5780          | 0.9453   |
| 0.0           | 76.0  | 304  | 0.5790          | 0.9453   |
| 0.0           | 77.0  | 308  | 0.5798          | 0.9453   |
| 0.0           | 78.0  | 312  | 0.5802          | 0.9453   |
| 0.0           | 79.0  | 316  | 0.5807          | 0.9453   |
| 0.0           | 80.0  | 320  | 0.5816          | 0.9453   |
| 0.0           | 81.0  | 324  | 0.5825          | 0.9453   |
| 0.0           | 82.0  | 328  | 0.5833          | 0.9453   |
| 0.0           | 83.0  | 332  | 0.5842          | 0.9453   |
| 0.0           | 84.0  | 336  | 0.5852          | 0.9453   |
| 0.0           | 85.0  | 340  | 0.5860          | 0.9453   |
| 0.0           | 86.0  | 344  | 0.5865          | 0.9453   |
| 0.0           | 87.0  | 348  | 0.5869          | 0.9453   |
| 0.0           | 88.0  | 352  | 0.5875          | 0.9453   |
| 0.0           | 89.0  | 356  | 0.5885          | 0.9453   |
| 0.0           | 90.0  | 360  | 0.5897          | 0.9453   |
| 0.0           | 91.0  | 364  | 0.5908          | 0.9453   |
| 0.0           | 92.0  | 368  | 0.5921          | 0.9453   |
| 0.0           | 93.0  | 372  | 0.5932          | 0.9453   |
| 0.0           | 94.0  | 376  | 0.5943          | 0.9453   |
| 0.0           | 95.0  | 380  | 0.5955          | 0.9453   |
| 0.0           | 96.0  | 384  | 0.5965          | 0.9453   |
| 0.0           | 97.0  | 388  | 0.5976          | 0.9453   |
| 0.0           | 98.0  | 392  | 0.5986          | 0.9453   |
| 0.0           | 99.0  | 396  | 0.5982          | 0.9453   |
| 0.0           | 100.0 | 400  | 0.5981          | 0.9453   |
| 0.0           | 101.0 | 404  | 0.5980          | 0.9453   |
| 0.0           | 102.0 | 408  | 0.5978          | 0.9453   |
| 0.0           | 103.0 | 412  | 0.5979          | 0.9453   |
| 0.0           | 104.0 | 416  | 0.5974          | 0.9453   |
| 0.0           | 105.0 | 420  | 0.5970          | 0.9453   |
| 0.0           | 106.0 | 424  | 0.5978          | 0.9453   |
| 0.0           | 107.0 | 428  | 0.5986          | 0.9453   |
| 0.0           | 108.0 | 432  | 0.5995          | 0.9453   |
| 0.0           | 109.0 | 436  | 0.6002          | 0.9453   |
| 0.0           | 110.0 | 440  | 0.6014          | 0.9453   |
| 0.0           | 111.0 | 444  | 0.6027          | 0.9453   |
| 0.0           | 112.0 | 448  | 0.6042          | 0.9453   |
| 0.0           | 113.0 | 452  | 0.6054          | 0.9453   |
| 0.0           | 114.0 | 456  | 0.6067          | 0.9453   |
| 0.0           | 115.0 | 460  | 0.6078          | 0.9453   |
| 0.0           | 116.0 | 464  | 0.6086          | 0.9453   |
| 0.0           | 117.0 | 468  | 0.6092          | 0.9453   |
| 0.0           | 118.0 | 472  | 0.6098          | 0.9453   |
| 0.0           | 119.0 | 476  | 0.6103          | 0.9453   |
| 0.0           | 120.0 | 480  | 0.6110          | 0.9453   |
| 0.0           | 121.0 | 484  | 0.6117          | 0.9453   |
| 0.0           | 122.0 | 488  | 0.6124          | 0.9453   |
| 0.0           | 123.0 | 492  | 0.6128          | 0.9453   |
| 0.0           | 124.0 | 496  | 0.6129          | 0.9453   |
| 0.0           | 125.0 | 500  | 0.6129          | 0.9453   |
| 0.0           | 126.0 | 504  | 0.6130          | 0.9453   |
| 0.0           | 127.0 | 508  | 0.6133          | 0.9453   |
| 0.0           | 128.0 | 512  | 0.6136          | 0.9453   |
| 0.0           | 129.0 | 516  | 0.6139          | 0.9453   |
| 0.0           | 130.0 | 520  | 0.6143          | 0.9453   |
| 0.0           | 131.0 | 524  | 0.6146          | 0.9453   |
| 0.0           | 132.0 | 528  | 0.6149          | 0.9453   |
| 0.0           | 133.0 | 532  | 0.6151          | 0.9453   |
| 0.0           | 134.0 | 536  | 0.6150          | 0.9453   |
| 0.0           | 135.0 | 540  | 0.6144          | 0.9453   |
| 0.0           | 136.0 | 544  | 0.6141          | 0.9453   |
| 0.0           | 137.0 | 548  | 0.6140          | 0.9453   |
| 0.0           | 138.0 | 552  | 0.6141          | 0.9453   |
| 0.0           | 139.0 | 556  | 0.6141          | 0.9453   |
| 0.0           | 140.0 | 560  | 0.6140          | 0.9453   |
| 0.0           | 141.0 | 564  | 0.6139          | 0.9453   |
| 0.0           | 142.0 | 568  | 0.6139          | 0.9453   |
| 0.0           | 143.0 | 572  | 0.6140          | 0.9453   |
| 0.0           | 144.0 | 576  | 0.6143          | 0.9453   |
| 0.0           | 145.0 | 580  | 0.6146          | 0.9453   |
| 0.0           | 146.0 | 584  | 0.6148          | 0.9453   |
| 0.0           | 147.0 | 588  | 0.6149          | 0.9453   |
| 0.0           | 148.0 | 592  | 0.6150          | 0.9453   |
| 0.0           | 149.0 | 596  | 0.6150          | 0.9453   |
| 0.0           | 150.0 | 600  | 0.6151          | 0.9453   |


### Framework versions

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