---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-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-yelp_polarity-64-21

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: 0.7237
- Accuracy: 0.9375

## 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    | 0.4953          | 0.9531   |
| No log        | 2.0   | 8    | 0.4901          | 0.9609   |
| 0.4161        | 3.0   | 12   | 0.4944          | 0.9609   |
| 0.4161        | 4.0   | 16   | 0.5364          | 0.9531   |
| 0.3982        | 5.0   | 20   | 0.5743          | 0.9531   |
| 0.3982        | 6.0   | 24   | 0.5888          | 0.9531   |
| 0.3982        | 7.0   | 28   | 0.5943          | 0.9531   |
| 0.271         | 8.0   | 32   | 0.5953          | 0.9531   |
| 0.271         | 9.0   | 36   | 0.5948          | 0.9531   |
| 0.3643        | 10.0  | 40   | 0.5942          | 0.9531   |
| 0.3643        | 11.0  | 44   | 0.5936          | 0.9531   |
| 0.3643        | 12.0  | 48   | 0.5918          | 0.9531   |
| 0.2103        | 13.0  | 52   | 0.5912          | 0.9531   |
| 0.2103        | 14.0  | 56   | 0.5900          | 0.9531   |
| 0.1932        | 15.0  | 60   | 0.5847          | 0.9531   |
| 0.1932        | 16.0  | 64   | 0.5810          | 0.9531   |
| 0.1932        | 17.0  | 68   | 0.5774          | 0.9531   |
| 0.1372        | 18.0  | 72   | 0.5731          | 0.9531   |
| 0.1372        | 19.0  | 76   | 0.5691          | 0.9531   |
| 0.0774        | 20.0  | 80   | 0.5697          | 0.9531   |
| 0.0774        | 21.0  | 84   | 0.5627          | 0.9531   |
| 0.0774        | 22.0  | 88   | 0.5599          | 0.9531   |
| 0.0831        | 23.0  | 92   | 0.5587          | 0.9531   |
| 0.0831        | 24.0  | 96   | 0.5821          | 0.9453   |
| 0.0236        | 25.0  | 100  | 0.5533          | 0.9531   |
| 0.0236        | 26.0  | 104  | 0.5497          | 0.9531   |
| 0.0236        | 27.0  | 108  | 0.5459          | 0.9531   |
| 0.0245        | 28.0  | 112  | 0.5447          | 0.9531   |
| 0.0245        | 29.0  | 116  | 0.5385          | 0.9531   |
| 0.0123        | 30.0  | 120  | 0.5433          | 0.9453   |
| 0.0123        | 31.0  | 124  | 0.5401          | 0.9453   |
| 0.0123        | 32.0  | 128  | 0.5369          | 0.9453   |
| 0.0           | 33.0  | 132  | 0.5347          | 0.9453   |
| 0.0           | 34.0  | 136  | 0.5363          | 0.9453   |
| 0.0001        | 35.0  | 140  | 0.5268          | 0.9531   |
| 0.0001        | 36.0  | 144  | 0.5327          | 0.9531   |
| 0.0001        | 37.0  | 148  | 0.5355          | 0.9531   |
| 0.0           | 38.0  | 152  | 0.5369          | 0.9531   |
| 0.0           | 39.0  | 156  | 0.5374          | 0.9531   |
| 0.0           | 40.0  | 160  | 0.5374          | 0.9531   |
| 0.0           | 41.0  | 164  | 0.5372          | 0.9531   |
| 0.0           | 42.0  | 168  | 0.5366          | 0.9531   |
| 0.0           | 43.0  | 172  | 0.5345          | 0.9531   |
| 0.0           | 44.0  | 176  | 0.5323          | 0.9531   |
| 0.0           | 45.0  | 180  | 0.5295          | 0.9453   |
| 0.0           | 46.0  | 184  | 0.5441          | 0.9453   |
| 0.0           | 47.0  | 188  | 0.5519          | 0.9453   |
| 0.0           | 48.0  | 192  | 0.5562          | 0.9453   |
| 0.0           | 49.0  | 196  | 0.5588          | 0.9453   |
| 0.0           | 50.0  | 200  | 0.5607          | 0.9453   |
| 0.0           | 51.0  | 204  | 0.5622          | 0.9453   |
| 0.0           | 52.0  | 208  | 0.5632          | 0.9453   |
| 0.0           | 53.0  | 212  | 0.5640          | 0.9453   |
| 0.0           | 54.0  | 216  | 0.5660          | 0.9453   |
| 0.0001        | 55.0  | 220  | 0.5577          | 0.9531   |
| 0.0001        | 56.0  | 224  | 0.6090          | 0.9453   |
| 0.0001        | 57.0  | 228  | 0.5699          | 0.9453   |
| 0.0           | 58.0  | 232  | 0.5844          | 0.9453   |
| 0.0           | 59.0  | 236  | 0.6061          | 0.9375   |
| 0.0318        | 60.0  | 240  | 0.5903          | 0.9453   |
| 0.0318        | 61.0  | 244  | 0.5835          | 0.9453   |
| 0.0318        | 62.0  | 248  | 0.5701          | 0.9453   |
| 0.0           | 63.0  | 252  | 0.5625          | 0.9531   |
| 0.0           | 64.0  | 256  | 0.5609          | 0.9531   |
| 0.0           | 65.0  | 260  | 0.5609          | 0.9531   |
| 0.0           | 66.0  | 264  | 0.5975          | 0.9375   |
| 0.0           | 67.0  | 268  | 0.6321          | 0.9297   |
| 0.0194        | 68.0  | 272  | 0.6293          | 0.9375   |
| 0.0194        | 69.0  | 276  | 0.6356          | 0.9297   |
| 0.0134        | 70.0  | 280  | 0.5923          | 0.9453   |
| 0.0134        | 71.0  | 284  | 0.5733          | 0.9453   |
| 0.0134        | 72.0  | 288  | 0.5553          | 0.9531   |
| 0.0           | 73.0  | 292  | 0.5595          | 0.9453   |
| 0.0           | 74.0  | 296  | 0.5778          | 0.9453   |
| 0.0001        | 75.0  | 300  | 0.6930          | 0.9297   |
| 0.0001        | 76.0  | 304  | 0.6281          | 0.9375   |
| 0.0001        | 77.0  | 308  | 0.6218          | 0.9375   |
| 0.0018        | 78.0  | 312  | 0.5614          | 0.9453   |
| 0.0018        | 79.0  | 316  | 0.5087          | 0.9531   |
| 0.0206        | 80.0  | 320  | 0.4872          | 0.9531   |
| 0.0206        | 81.0  | 324  | 0.4978          | 0.9531   |
| 0.0206        | 82.0  | 328  | 0.5067          | 0.9531   |
| 0.0           | 83.0  | 332  | 0.5116          | 0.9531   |
| 0.0           | 84.0  | 336  | 0.5143          | 0.9531   |
| 0.0           | 85.0  | 340  | 0.5159          | 0.9531   |
| 0.0           | 86.0  | 344  | 0.5175          | 0.9531   |
| 0.0           | 87.0  | 348  | 0.5206          | 0.9531   |
| 0.0           | 88.0  | 352  | 0.5255          | 0.9453   |
| 0.0           | 89.0  | 356  | 0.5319          | 0.9453   |
| 0.0           | 90.0  | 360  | 0.5390          | 0.9375   |
| 0.0           | 91.0  | 364  | 0.5455          | 0.9375   |
| 0.0           | 92.0  | 368  | 0.5516          | 0.9375   |
| 0.0           | 93.0  | 372  | 0.5572          | 0.9375   |
| 0.0           | 94.0  | 376  | 0.5623          | 0.9375   |
| 0.0           | 95.0  | 380  | 0.5664          | 0.9375   |
| 0.0           | 96.0  | 384  | 0.5692          | 0.9375   |
| 0.0           | 97.0  | 388  | 0.5712          | 0.9375   |
| 0.0           | 98.0  | 392  | 0.5734          | 0.9375   |
| 0.0           | 99.0  | 396  | 0.5754          | 0.9375   |
| 0.0           | 100.0 | 400  | 0.5765          | 0.9375   |
| 0.0           | 101.0 | 404  | 0.5815          | 0.9375   |
| 0.0           | 102.0 | 408  | 0.5821          | 0.9375   |
| 0.0           | 103.0 | 412  | 0.5819          | 0.9375   |
| 0.0           | 104.0 | 416  | 0.5818          | 0.9375   |
| 0.0           | 105.0 | 420  | 0.5805          | 0.9375   |
| 0.0           | 106.0 | 424  | 0.5984          | 0.9375   |
| 0.0           | 107.0 | 428  | 0.5581          | 0.9453   |
| 0.0231        | 108.0 | 432  | 0.5229          | 0.9531   |
| 0.0231        | 109.0 | 436  | 0.4868          | 0.9453   |
| 0.0           | 110.0 | 440  | 0.5184          | 0.9531   |
| 0.0           | 111.0 | 444  | 0.5554          | 0.9453   |
| 0.0           | 112.0 | 448  | 0.7197          | 0.9375   |
| 0.0001        | 113.0 | 452  | 0.7466          | 0.9375   |
| 0.0001        | 114.0 | 456  | 0.7533          | 0.9375   |
| 0.0           | 115.0 | 460  | 0.7535          | 0.9375   |
| 0.0           | 116.0 | 464  | 0.7472          | 0.9375   |
| 0.0           | 117.0 | 468  | 0.7407          | 0.9375   |
| 0.0           | 118.0 | 472  | 0.7366          | 0.9375   |
| 0.0           | 119.0 | 476  | 0.7347          | 0.9297   |
| 0.0           | 120.0 | 480  | 0.7338          | 0.9297   |
| 0.0           | 121.0 | 484  | 0.7366          | 0.9297   |
| 0.0           | 122.0 | 488  | 0.7408          | 0.9297   |
| 0.0           | 123.0 | 492  | 0.7434          | 0.9297   |
| 0.0           | 124.0 | 496  | 0.7454          | 0.9297   |
| 0.0073        | 125.0 | 500  | 0.6336          | 0.9453   |
| 0.0073        | 126.0 | 504  | 0.5907          | 0.9453   |
| 0.0073        | 127.0 | 508  | 0.6316          | 0.9453   |
| 0.0023        | 128.0 | 512  | 0.6673          | 0.9453   |
| 0.0023        | 129.0 | 516  | 0.6764          | 0.9453   |
| 0.0           | 130.0 | 520  | 0.6814          | 0.9453   |
| 0.0           | 131.0 | 524  | 0.6917          | 0.9375   |
| 0.0           | 132.0 | 528  | 0.7031          | 0.9375   |
| 0.0           | 133.0 | 532  | 0.7111          | 0.9375   |
| 0.0           | 134.0 | 536  | 0.7161          | 0.9375   |
| 0.0           | 135.0 | 540  | 0.7153          | 0.9375   |
| 0.0           | 136.0 | 544  | 0.7137          | 0.9375   |
| 0.0           | 137.0 | 548  | 0.7130          | 0.9375   |
| 0.0           | 138.0 | 552  | 0.7126          | 0.9375   |
| 0.0           | 139.0 | 556  | 0.7126          | 0.9375   |
| 0.0           | 140.0 | 560  | 0.7127          | 0.9375   |
| 0.0           | 141.0 | 564  | 0.7154          | 0.9375   |
| 0.0           | 142.0 | 568  | 0.7190          | 0.9375   |
| 0.0           | 143.0 | 572  | 0.7211          | 0.9375   |
| 0.0           | 144.0 | 576  | 0.7223          | 0.9375   |
| 0.0           | 145.0 | 580  | 0.7230          | 0.9375   |
| 0.0           | 146.0 | 584  | 0.7233          | 0.9375   |
| 0.0           | 147.0 | 588  | 0.7235          | 0.9375   |
| 0.0           | 148.0 | 592  | 0.7236          | 0.9375   |
| 0.0           | 149.0 | 596  | 0.7237          | 0.9375   |
| 0.0           | 150.0 | 600  | 0.7237          | 0.9375   |


### Framework versions

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