---
license: apache-2.0
base_model: albert-base-v2
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 [albert-base-v2](https://huggingface.co./albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6069
- 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    | 0.7342          | 0.9219   |
| No log        | 2.0   | 8    | 0.7290          | 0.9219   |
| 0.5102        | 3.0   | 12   | 0.7270          | 0.9219   |
| 0.5102        | 4.0   | 16   | 0.7253          | 0.9219   |
| 0.4089        | 5.0   | 20   | 0.7208          | 0.9219   |
| 0.4089        | 6.0   | 24   | 0.7191          | 0.9219   |
| 0.4089        | 7.0   | 28   | 0.7271          | 0.9297   |
| 0.3981        | 8.0   | 32   | 0.7192          | 0.9297   |
| 0.3981        | 9.0   | 36   | 0.7009          | 0.9219   |
| 0.1982        | 10.0  | 40   | 0.6963          | 0.9141   |
| 0.1982        | 11.0  | 44   | 0.6904          | 0.9219   |
| 0.1982        | 12.0  | 48   | 0.6924          | 0.9219   |
| 0.2128        | 13.0  | 52   | 0.6921          | 0.9297   |
| 0.2128        | 14.0  | 56   | 0.6866          | 0.9219   |
| 0.0935        | 15.0  | 60   | 0.6841          | 0.9219   |
| 0.0935        | 16.0  | 64   | 0.6494          | 0.9219   |
| 0.0935        | 17.0  | 68   | 0.6201          | 0.9219   |
| 0.0365        | 18.0  | 72   | 0.6122          | 0.9219   |
| 0.0365        | 19.0  | 76   | 0.6047          | 0.9219   |
| 0.026         | 20.0  | 80   | 0.5870          | 0.9219   |
| 0.026         | 21.0  | 84   | 0.5739          | 0.9219   |
| 0.026         | 22.0  | 88   | 0.5737          | 0.9219   |
| 0.0139        | 23.0  | 92   | 0.5677          | 0.9219   |
| 0.0139        | 24.0  | 96   | 0.5579          | 0.9219   |
| 0.0149        | 25.0  | 100  | 0.5468          | 0.9219   |
| 0.0149        | 26.0  | 104  | 0.5277          | 0.9219   |
| 0.0149        | 27.0  | 108  | 0.5168          | 0.9219   |
| 0.0085        | 28.0  | 112  | 0.5036          | 0.9141   |
| 0.0085        | 29.0  | 116  | 0.4960          | 0.9141   |
| 0.0           | 30.0  | 120  | 0.4941          | 0.9219   |
| 0.0           | 31.0  | 124  | 0.4956          | 0.9297   |
| 0.0           | 32.0  | 128  | 0.4987          | 0.9297   |
| 0.0           | 33.0  | 132  | 0.5018          | 0.9297   |
| 0.0           | 34.0  | 136  | 0.5053          | 0.9297   |
| 0.0           | 35.0  | 140  | 0.5081          | 0.9297   |
| 0.0           | 36.0  | 144  | 0.5107          | 0.9297   |
| 0.0           | 37.0  | 148  | 0.5125          | 0.9297   |
| 0.0           | 38.0  | 152  | 0.5135          | 0.9297   |
| 0.0           | 39.0  | 156  | 0.5146          | 0.9297   |
| 0.0           | 40.0  | 160  | 0.5157          | 0.9297   |
| 0.0           | 41.0  | 164  | 0.5168          | 0.9297   |
| 0.0           | 42.0  | 168  | 0.5182          | 0.9297   |
| 0.0           | 43.0  | 172  | 0.5197          | 0.9297   |
| 0.0           | 44.0  | 176  | 0.5209          | 0.9297   |
| 0.0           | 45.0  | 180  | 0.5224          | 0.9297   |
| 0.0           | 46.0  | 184  | 0.5240          | 0.9297   |
| 0.0           | 47.0  | 188  | 0.5257          | 0.9297   |
| 0.0           | 48.0  | 192  | 0.5272          | 0.9297   |
| 0.0           | 49.0  | 196  | 0.5286          | 0.9297   |
| 0.0           | 50.0  | 200  | 0.5300          | 0.9297   |
| 0.0           | 51.0  | 204  | 0.5313          | 0.9297   |
| 0.0           | 52.0  | 208  | 0.5329          | 0.9297   |
| 0.0           | 53.0  | 212  | 0.5343          | 0.9297   |
| 0.0           | 54.0  | 216  | 0.5355          | 0.9297   |
| 0.0           | 55.0  | 220  | 0.5369          | 0.9297   |
| 0.0           | 56.0  | 224  | 0.5382          | 0.9297   |
| 0.0           | 57.0  | 228  | 0.5395          | 0.9297   |
| 0.0           | 58.0  | 232  | 0.5407          | 0.9297   |
| 0.0           | 59.0  | 236  | 0.5419          | 0.9297   |
| 0.0           | 60.0  | 240  | 0.5431          | 0.9297   |
| 0.0           | 61.0  | 244  | 0.5444          | 0.9297   |
| 0.0           | 62.0  | 248  | 0.5455          | 0.9297   |
| 0.0           | 63.0  | 252  | 0.5466          | 0.9297   |
| 0.0           | 64.0  | 256  | 0.5478          | 0.9297   |
| 0.0           | 65.0  | 260  | 0.5489          | 0.9297   |
| 0.0           | 66.0  | 264  | 0.5501          | 0.9297   |
| 0.0           | 67.0  | 268  | 0.5513          | 0.9297   |
| 0.0           | 68.0  | 272  | 0.5524          | 0.9297   |
| 0.0           | 69.0  | 276  | 0.5535          | 0.9297   |
| 0.0           | 70.0  | 280  | 0.5548          | 0.9297   |
| 0.0           | 71.0  | 284  | 0.5559          | 0.9297   |
| 0.0           | 72.0  | 288  | 0.5570          | 0.9297   |
| 0.0           | 73.0  | 292  | 0.5581          | 0.9297   |
| 0.0           | 74.0  | 296  | 0.5592          | 0.9297   |
| 0.0           | 75.0  | 300  | 0.5601          | 0.9297   |
| 0.0           | 76.0  | 304  | 0.5610          | 0.9297   |
| 0.0           | 77.0  | 308  | 0.5620          | 0.9297   |
| 0.0           | 78.0  | 312  | 0.5630          | 0.9297   |
| 0.0           | 79.0  | 316  | 0.5640          | 0.9297   |
| 0.0           | 80.0  | 320  | 0.5648          | 0.9297   |
| 0.0           | 81.0  | 324  | 0.5658          | 0.9297   |
| 0.0           | 82.0  | 328  | 0.5667          | 0.9297   |
| 0.0           | 83.0  | 332  | 0.5675          | 0.9297   |
| 0.0           | 84.0  | 336  | 0.5684          | 0.9297   |
| 0.0           | 85.0  | 340  | 0.5693          | 0.9297   |
| 0.0           | 86.0  | 344  | 0.5701          | 0.9297   |
| 0.0           | 87.0  | 348  | 0.5710          | 0.9297   |
| 0.0           | 88.0  | 352  | 0.5719          | 0.9297   |
| 0.0           | 89.0  | 356  | 0.5728          | 0.9297   |
| 0.0           | 90.0  | 360  | 0.5736          | 0.9297   |
| 0.0           | 91.0  | 364  | 0.5745          | 0.9297   |
| 0.0           | 92.0  | 368  | 0.5754          | 0.9297   |
| 0.0           | 93.0  | 372  | 0.5762          | 0.9297   |
| 0.0           | 94.0  | 376  | 0.5771          | 0.9297   |
| 0.0           | 95.0  | 380  | 0.5779          | 0.9297   |
| 0.0           | 96.0  | 384  | 0.5788          | 0.9297   |
| 0.0           | 97.0  | 388  | 0.5796          | 0.9297   |
| 0.0           | 98.0  | 392  | 0.5804          | 0.9297   |
| 0.0           | 99.0  | 396  | 0.5812          | 0.9297   |
| 0.0           | 100.0 | 400  | 0.5820          | 0.9297   |
| 0.0           | 101.0 | 404  | 0.5828          | 0.9297   |
| 0.0           | 102.0 | 408  | 0.5836          | 0.9297   |
| 0.0           | 103.0 | 412  | 0.5843          | 0.9297   |
| 0.0           | 104.0 | 416  | 0.5851          | 0.9297   |
| 0.0           | 105.0 | 420  | 0.5859          | 0.9297   |
| 0.0           | 106.0 | 424  | 0.5866          | 0.9297   |
| 0.0           | 107.0 | 428  | 0.5874          | 0.9297   |
| 0.0           | 108.0 | 432  | 0.5881          | 0.9297   |
| 0.0           | 109.0 | 436  | 0.5889          | 0.9297   |
| 0.0           | 110.0 | 440  | 0.5896          | 0.9297   |
| 0.0           | 111.0 | 444  | 0.5902          | 0.9297   |
| 0.0           | 112.0 | 448  | 0.5910          | 0.9375   |
| 0.0           | 113.0 | 452  | 0.5916          | 0.9375   |
| 0.0           | 114.0 | 456  | 0.5924          | 0.9375   |
| 0.0           | 115.0 | 460  | 0.5931          | 0.9375   |
| 0.0           | 116.0 | 464  | 0.5938          | 0.9375   |
| 0.0           | 117.0 | 468  | 0.5945          | 0.9375   |
| 0.0           | 118.0 | 472  | 0.5952          | 0.9375   |
| 0.0           | 119.0 | 476  | 0.5958          | 0.9375   |
| 0.0           | 120.0 | 480  | 0.5964          | 0.9375   |
| 0.0           | 121.0 | 484  | 0.5971          | 0.9375   |
| 0.0           | 122.0 | 488  | 0.5978          | 0.9375   |
| 0.0           | 123.0 | 492  | 0.5985          | 0.9375   |
| 0.0           | 124.0 | 496  | 0.5991          | 0.9375   |
| 0.0           | 125.0 | 500  | 0.5997          | 0.9375   |
| 0.0           | 126.0 | 504  | 0.6004          | 0.9375   |
| 0.0           | 127.0 | 508  | 0.6009          | 0.9375   |
| 0.0           | 128.0 | 512  | 0.6015          | 0.9375   |
| 0.0           | 129.0 | 516  | 0.6020          | 0.9375   |
| 0.0           | 130.0 | 520  | 0.6025          | 0.9375   |
| 0.0           | 131.0 | 524  | 0.6029          | 0.9375   |
| 0.0           | 132.0 | 528  | 0.6034          | 0.9375   |
| 0.0           | 133.0 | 532  | 0.6038          | 0.9375   |
| 0.0           | 134.0 | 536  | 0.6042          | 0.9375   |
| 0.0           | 135.0 | 540  | 0.6045          | 0.9375   |
| 0.0           | 136.0 | 544  | 0.6048          | 0.9375   |
| 0.0           | 137.0 | 548  | 0.6051          | 0.9375   |
| 0.0           | 138.0 | 552  | 0.6054          | 0.9375   |
| 0.0           | 139.0 | 556  | 0.6056          | 0.9375   |
| 0.0           | 140.0 | 560  | 0.6058          | 0.9375   |
| 0.0           | 141.0 | 564  | 0.6061          | 0.9375   |
| 0.0           | 142.0 | 568  | 0.6062          | 0.9375   |
| 0.0           | 143.0 | 572  | 0.6064          | 0.9375   |
| 0.0           | 144.0 | 576  | 0.6065          | 0.9375   |
| 0.0           | 145.0 | 580  | 0.6066          | 0.9375   |
| 0.0           | 146.0 | 584  | 0.6067          | 0.9375   |
| 0.0           | 147.0 | 588  | 0.6068          | 0.9375   |
| 0.0           | 148.0 | 592  | 0.6068          | 0.9375   |
| 0.0           | 149.0 | 596  | 0.6069          | 0.9375   |
| 0.0           | 150.0 | 600  | 0.6069          | 0.9375   |


### Framework versions

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