readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - yelp_review_full
  metrics:
  - accuracy
  model-index:
  - name: YELP_ALBERT_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: yelp_review_full
        type: yelp_review_full
        config: yelp_review_full
        split: train
        args: yelp_review_full
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9733333333333334
  ---
  
  <!-- 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. -->
  
  # YELP_ALBERT_5E
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1394
  - Accuracy: 0.9733
  
  ## 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: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4967        | 0.03  | 50   | 0.1667          | 0.9467   |
  | 0.3268        | 0.06  | 100  | 0.2106          | 0.9133   |
  | 0.3413        | 0.1   | 150  | 0.2107          | 0.9667   |
  | 0.3172        | 0.13  | 200  | 0.1906          | 0.94     |
  | 0.2804        | 0.16  | 250  | 0.2588          | 0.9      |
  | 0.2604        | 0.19  | 300  | 0.2023          | 0.94     |
  | 0.2532        | 0.22  | 350  | 0.1263          | 0.9533   |
  | 0.2103        | 0.26  | 400  | 0.1233          | 0.96     |
  | 0.212         | 0.29  | 450  | 0.2019          | 0.9267   |
  | 0.2669        | 0.32  | 500  | 0.1110          | 0.9667   |
  | 0.2187        | 0.35  | 550  | 0.1542          | 0.96     |
  | 0.2203        | 0.38  | 600  | 0.0879          | 0.9733   |
  | 0.2699        | 0.42  | 650  | 0.0971          | 0.9667   |
  | 0.2107        | 0.45  | 700  | 0.0863          | 0.9667   |
  | 0.2443        | 0.48  | 750  | 0.0823          | 0.9733   |
  | 0.1987        | 0.51  | 800  | 0.1207          | 0.9733   |
  | 0.2326        | 0.54  | 850  | 0.1368          | 0.9667   |
  | 0.1787        | 0.58  | 900  | 0.1027          | 0.9667   |
  | 0.2159        | 0.61  | 950  | 0.2443          | 0.9333   |
  | 0.1316        | 0.64  | 1000 | 0.2035          | 0.9467   |
  | 0.2416        | 0.67  | 1050 | 0.0882          | 0.9733   |
  | 0.2008        | 0.7   | 1100 | 0.1709          | 0.9533   |
  | 0.2065        | 0.74  | 1150 | 0.1098          | 0.9667   |
  | 0.2391        | 0.77  | 1200 | 0.1055          | 0.9667   |
  | 0.1533        | 0.8   | 1250 | 0.1997          | 0.94     |
  | 0.2016        | 0.83  | 1300 | 0.0899          | 0.96     |
  | 0.2016        | 0.86  | 1350 | 0.0957          | 0.9733   |
  | 0.2316        | 0.9   | 1400 | 0.0784          | 0.98     |
  | 0.1839        | 0.93  | 1450 | 0.0784          | 0.9733   |
  | 0.2121        | 0.96  | 1500 | 0.1150          | 0.9733   |
  | 0.1307        | 0.99  | 1550 | 0.0969          | 0.9733   |
  | 0.1271        | 1.02  | 1600 | 0.2326          | 0.9467   |
  | 0.1736        | 1.06  | 1650 | 0.0979          | 0.9667   |
  | 0.1357        | 1.09  | 1700 | 0.0862          | 0.98     |
  | 0.1871        | 1.12  | 1750 | 0.1419          | 0.9667   |
  | 0.1411        | 1.15  | 1800 | 0.1301          | 0.96     |
  | 0.1317        | 1.18  | 1850 | 0.1602          | 0.9533   |
  | 0.1432        | 1.22  | 1900 | 0.1885          | 0.9533   |
  | 0.1793        | 1.25  | 1950 | 0.0776          | 0.9667   |
  | 0.1322        | 1.28  | 2000 | 0.0822          | 0.9733   |
  | 0.1416        | 1.31  | 2050 | 0.0920          | 0.9733   |
  | 0.1524        | 1.34  | 2100 | 0.0673          | 0.98     |
  | 0.1338        | 1.38  | 2150 | 0.0602          | 0.98     |
  | 0.152         | 1.41  | 2200 | 0.0916          | 0.98     |
  | 0.1192        | 1.44  | 2250 | 0.0559          | 0.98     |
  | 0.1471        | 1.47  | 2300 | 0.1096          | 0.9667   |
  | 0.1267        | 1.5   | 2350 | 0.0695          | 0.9733   |
  | 0.1776        | 1.54  | 2400 | 0.1363          | 0.96     |
  | 0.1495        | 1.57  | 2450 | 0.0818          | 0.98     |
  | 0.1158        | 1.6   | 2500 | 0.1282          | 0.9667   |
  | 0.1772        | 1.63  | 2550 | 0.0682          | 0.9733   |
  | 0.1187        | 1.66  | 2600 | 0.1032          | 0.9733   |
  | 0.136         | 1.7   | 2650 | 0.1071          | 0.9667   |
  | 0.1829        | 1.73  | 2700 | 0.0753          | 0.9667   |
  | 0.1147        | 1.76  | 2750 | 0.1071          | 0.9733   |
  | 0.1174        | 1.79  | 2800 | 0.1441          | 0.9667   |
  | 0.0707        | 1.82  | 2850 | 0.1