---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-42
  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-64-42

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: 0.8838
- Accuracy: 0.9141

## 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    | 1.0394          | 0.9141   |
| No log        | 2.0   | 8    | 1.0413          | 0.9141   |
| 0.5047        | 3.0   | 12   | 1.0408          | 0.9141   |
| 0.5047        | 4.0   | 16   | 1.0386          | 0.9141   |
| 0.4566        | 5.0   | 20   | 1.0336          | 0.9141   |
| 0.4566        | 6.0   | 24   | 1.0248          | 0.9141   |
| 0.4566        | 7.0   | 28   | 1.0128          | 0.9141   |
| 0.4026        | 8.0   | 32   | 1.0000          | 0.9141   |
| 0.4026        | 9.0   | 36   | 0.9823          | 0.9141   |
| 0.3103        | 10.0  | 40   | 0.9632          | 0.9141   |
| 0.3103        | 11.0  | 44   | 0.9553          | 0.9219   |
| 0.3103        | 12.0  | 48   | 0.9610          | 0.9141   |
| 0.2537        | 13.0  | 52   | 0.9575          | 0.9141   |
| 0.2537        | 14.0  | 56   | 0.9497          | 0.9141   |
| 0.1335        | 15.0  | 60   | 0.9510          | 0.9141   |
| 0.1335        | 16.0  | 64   | 0.9465          | 0.9141   |
| 0.1335        | 17.0  | 68   | 0.9379          | 0.9141   |
| 0.0655        | 18.0  | 72   | 0.9312          | 0.9141   |
| 0.0655        | 19.0  | 76   | 0.9317          | 0.9141   |
| 0.051         | 20.0  | 80   | 0.9246          | 0.9141   |
| 0.051         | 21.0  | 84   | 0.9026          | 0.9141   |
| 0.051         | 22.0  | 88   | 0.8836          | 0.9141   |
| 0.0012        | 23.0  | 92   | 0.8697          | 0.9141   |
| 0.0012        | 24.0  | 96   | 0.8588          | 0.9141   |
| 0.0003        | 25.0  | 100  | 0.8458          | 0.9141   |
| 0.0003        | 26.0  | 104  | 0.8323          | 0.9141   |
| 0.0003        | 27.0  | 108  | 0.8499          | 0.9141   |
| 0.0019        | 28.0  | 112  | 0.8750          | 0.9219   |
| 0.0019        | 29.0  | 116  | 0.8897          | 0.9219   |
| 0.0           | 30.0  | 120  | 0.8943          | 0.9219   |
| 0.0           | 31.0  | 124  | 0.8570          | 0.9219   |
| 0.0           | 32.0  | 128  | 0.8162          | 0.9219   |
| 0.0065        | 33.0  | 132  | 0.8156          | 0.9141   |
| 0.0065        | 34.0  | 136  | 0.8147          | 0.9141   |
| 0.0137        | 35.0  | 140  | 0.8191          | 0.9219   |
| 0.0137        | 36.0  | 144  | 0.8258          | 0.9219   |
| 0.0137        | 37.0  | 148  | 0.8316          | 0.9141   |
| 0.0           | 38.0  | 152  | 0.8362          | 0.9219   |
| 0.0           | 39.0  | 156  | 0.8188          | 0.9141   |
| 0.0001        | 40.0  | 160  | 0.8255          | 0.9141   |
| 0.0001        | 41.0  | 164  | 0.8535          | 0.9062   |
| 0.0001        | 42.0  | 168  | 0.8499          | 0.9062   |
| 0.0017        | 43.0  | 172  | 0.8184          | 0.9141   |
| 0.0017        | 44.0  | 176  | 0.8120          | 0.9297   |
| 0.0           | 45.0  | 180  | 0.8277          | 0.9219   |
| 0.0           | 46.0  | 184  | 0.8434          | 0.9219   |
| 0.0           | 47.0  | 188  | 0.8535          | 0.9219   |
| 0.0           | 48.0  | 192  | 0.8597          | 0.9219   |
| 0.0           | 49.0  | 196  | 0.8633          | 0.9219   |
| 0.0           | 50.0  | 200  | 0.8651          | 0.9219   |
| 0.0           | 51.0  | 204  | 0.8617          | 0.9219   |
| 0.0           | 52.0  | 208  | 0.8571          | 0.9219   |
| 0.0           | 53.0  | 212  | 0.8538          | 0.9219   |
| 0.0           | 54.0  | 216  | 0.8514          | 0.9219   |
| 0.0           | 55.0  | 220  | 0.8346          | 0.9219   |
| 0.0           | 56.0  | 224  | 0.8153          | 0.9219   |
| 0.0           | 57.0  | 228  | 0.8087          | 0.9219   |
| 0.0           | 58.0  | 232  | 0.8083          | 0.9141   |
| 0.0           | 59.0  | 236  | 0.8168          | 0.9141   |
| 0.0002        | 60.0  | 240  | 0.8424          | 0.9141   |
| 0.0002        | 61.0  | 244  | 0.8614          | 0.9141   |
| 0.0002        | 62.0  | 248  | 0.8736          | 0.9141   |
| 0.0           | 63.0  | 252  | 0.8817          | 0.9141   |
| 0.0           | 64.0  | 256  | 0.8848          | 0.9141   |
| 0.0           | 65.0  | 260  | 0.8876          | 0.9141   |
| 0.0           | 66.0  | 264  | 0.8896          | 0.9141   |
| 0.0           | 67.0  | 268  | 0.8868          | 0.9141   |
| 0.0           | 68.0  | 272  | 0.8831          | 0.9141   |
| 0.0           | 69.0  | 276  | 0.8792          | 0.9141   |
| 0.0001        | 70.0  | 280  | 0.8107          | 0.9141   |
| 0.0001        | 71.0  | 284  | 0.9166          | 0.9219   |
| 0.0001        | 72.0  | 288  | 0.8786          | 0.9219   |
| 0.0232        | 73.0  | 292  | 0.8429          | 0.9219   |
| 0.0232        | 74.0  | 296  | 0.8228          | 0.9297   |
| 0.0           | 75.0  | 300  | 0.8332          | 0.9219   |
| 0.0           | 76.0  | 304  | 0.8651          | 0.9062   |
| 0.0           | 77.0  | 308  | 0.8879          | 0.9062   |
| 0.0           | 78.0  | 312  | 0.9017          | 0.9062   |
| 0.0           | 79.0  | 316  | 0.9093          | 0.9062   |
| 0.0           | 80.0  | 320  | 0.9133          | 0.9062   |
| 0.0           | 81.0  | 324  | 0.9160          | 0.9062   |
| 0.0           | 82.0  | 328  | 0.9180          | 0.9062   |
| 0.0           | 83.0  | 332  | 0.9192          | 0.9062   |
| 0.0           | 84.0  | 336  | 0.9196          | 0.9062   |
| 0.0           | 85.0  | 340  | 0.9209          | 0.9062   |
| 0.0           | 86.0  | 344  | 0.9250          | 0.9062   |
| 0.0           | 87.0  | 348  | 0.9289          | 0.9062   |
| 0.0           | 88.0  | 352  | 0.9314          | 0.9062   |
| 0.0           | 89.0  | 356  | 0.9330          | 0.9062   |
| 0.0           | 90.0  | 360  | 0.9340          | 0.9062   |
| 0.0           | 91.0  | 364  | 0.9346          | 0.9062   |
| 0.0           | 92.0  | 368  | 0.9348          | 0.9062   |
| 0.0           | 93.0  | 372  | 0.9351          | 0.9062   |
| 0.0           | 94.0  | 376  | 0.9354          | 0.9062   |
| 0.0           | 95.0  | 380  | 0.9355          | 0.9062   |
| 0.0           | 96.0  | 384  | 0.9354          | 0.9062   |
| 0.0           | 97.0  | 388  | 0.9339          | 0.9062   |
| 0.0           | 98.0  | 392  | 0.9310          | 0.9062   |
| 0.0           | 99.0  | 396  | 0.9290          | 0.9062   |
| 0.0           | 100.0 | 400  | 0.9276          | 0.9062   |
| 0.0           | 101.0 | 404  | 0.9271          | 0.9062   |
| 0.0           | 102.0 | 408  | 0.9274          | 0.9062   |
| 0.0           | 103.0 | 412  | 0.9277          | 0.9062   |
| 0.0           | 104.0 | 416  | 0.9282          | 0.9062   |
| 0.0           | 105.0 | 420  | 0.9285          | 0.9062   |
| 0.0           | 106.0 | 424  | 0.9289          | 0.9062   |
| 0.0           | 107.0 | 428  | 0.9293          | 0.9062   |
| 0.0           | 108.0 | 432  | 0.9297          | 0.9062   |
| 0.0           | 109.0 | 436  | 0.9296          | 0.9062   |
| 0.0           | 110.0 | 440  | 0.9297          | 0.9062   |
| 0.0           | 111.0 | 444  | 0.9328          | 0.9062   |
| 0.0           | 112.0 | 448  | 0.9376          | 0.9062   |
| 0.0           | 113.0 | 452  | 0.9408          | 0.9062   |
| 0.0           | 114.0 | 456  | 0.9428          | 0.9062   |
| 0.0           | 115.0 | 460  | 0.9442          | 0.9062   |
| 0.0           | 116.0 | 464  | 0.9455          | 0.9062   |
| 0.0           | 117.0 | 468  | 0.9464          | 0.9062   |
| 0.0           | 118.0 | 472  | 0.9470          | 0.9062   |
| 0.0           | 119.0 | 476  | 0.9478          | 0.9062   |
| 0.0           | 120.0 | 480  | 0.9487          | 0.9062   |
| 0.0           | 121.0 | 484  | 0.9492          | 0.9062   |
| 0.0           | 122.0 | 488  | 0.9496          | 0.9062   |
| 0.0           | 123.0 | 492  | 0.9499          | 0.9062   |
| 0.0           | 124.0 | 496  | 0.9504          | 0.9062   |
| 0.0           | 125.0 | 500  | 0.9505          | 0.9062   |
| 0.0           | 126.0 | 504  | 0.9507          | 0.9062   |
| 0.0           | 127.0 | 508  | 0.9509          | 0.9062   |
| 0.0           | 128.0 | 512  | 0.9504          | 0.9062   |
| 0.0           | 129.0 | 516  | 0.9502          | 0.9062   |
| 0.0           | 130.0 | 520  | 0.9500          | 0.9062   |
| 0.0           | 131.0 | 524  | 0.9497          | 0.9062   |
| 0.0           | 132.0 | 528  | 0.9496          | 0.9062   |
| 0.0           | 133.0 | 532  | 0.9496          | 0.9062   |
| 0.0           | 134.0 | 536  | 0.9498          | 0.9062   |
| 0.0           | 135.0 | 540  | 0.9502          | 0.9062   |
| 0.0           | 136.0 | 544  | 0.9398          | 0.9062   |
| 0.0           | 137.0 | 548  | 0.9199          | 0.9062   |
| 0.0           | 138.0 | 552  | 0.9047          | 0.9062   |
| 0.0           | 139.0 | 556  | 0.8950          | 0.9141   |
| 0.0           | 140.0 | 560  | 0.8894          | 0.9141   |
| 0.0           | 141.0 | 564  | 0.8862          | 0.9141   |
| 0.0           | 142.0 | 568  | 0.8846          | 0.9141   |
| 0.0           | 143.0 | 572  | 0.8840          | 0.9141   |
| 0.0           | 144.0 | 576  | 0.8837          | 0.9141   |
| 0.0           | 145.0 | 580  | 0.8836          | 0.9141   |
| 0.0           | 146.0 | 584  | 0.8836          | 0.9141   |
| 0.0           | 147.0 | 588  | 0.8837          | 0.9141   |
| 0.0           | 148.0 | 592  | 0.8838          | 0.9141   |
| 0.0           | 149.0 | 596  | 0.8838          | 0.9141   |
| 0.0           | 150.0 | 600  | 0.8838          | 0.9141   |


### Framework versions

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