readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_ALBERT_5E
    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. -->
  
  # TSE_ALBERT_5E
  
  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: 0.3667
  - Accuracy: 0.9333
  
  ## 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5712        | 0.06  | 50   | 0.4047          | 0.82     |
  | 0.3198        | 0.12  | 100  | 0.2883          | 0.9      |
  | 0.3254        | 0.17  | 150  | 0.4352          | 0.84     |
  | 0.2898        | 0.23  | 200  | 0.2892          | 0.9133   |
  | 0.2826        | 0.29  | 250  | 0.3565          | 0.8867   |
  | 0.2696        | 0.35  | 300  | 0.2263          | 0.9333   |
  | 0.274         | 0.4   | 350  | 0.2068          | 0.94     |
  | 0.2393        | 0.46  | 400  | 0.2270          | 0.9333   |
  | 0.2067        | 0.52  | 450  | 0.2118          | 0.9333   |
  | 0.2332        | 0.58  | 500  | 0.4454          | 0.88     |
  | 0.3099        | 0.63  | 550  | 0.2777          | 0.9067   |
  | 0.2687        | 0.69  | 600  | 0.2077          | 0.9333   |
  | 0.2053        | 0.75  | 650  | 0.1923          | 0.9533   |
  | 0.2359        | 0.81  | 700  | 0.3891          | 0.9067   |
  | 0.2492        | 0.87  | 750  | 0.2765          | 0.9333   |
  | 0.2589        | 0.92  | 800  | 0.1879          | 0.9467   |
  | 0.2161        | 0.98  | 850  | 0.2733          | 0.9267   |
  | 0.1752        | 1.04  | 900  | 0.3108          | 0.92     |
  | 0.2213        | 1.1   | 950  | 0.3318          | 0.92     |
  | 0.1665        | 1.15  | 1000 | 0.4124          | 0.8933   |
  | 0.1832        | 1.21  | 1050 | 0.3448          | 0.92     |
  | 0.1671        | 1.27  | 1100 | 0.3343          | 0.9067   |
  | 0.184         | 1.33  | 1150 | 0.3929          | 0.9067   |
  | 0.2788        | 1.38  | 1200 | 0.3888          | 0.8933   |
  | 0.1768        | 1.44  | 1250 | 0.4028          | 0.9      |
  | 0.2368        | 1.5   | 1300 | 0.3154          | 0.9133   |
  | 0.2055        | 1.56  | 1350 | 0.2603          | 0.9267   |
  | 0.1693        | 1.61  | 1400 | 0.2994          | 0.9267   |
  | 0.1447        | 1.67  | 1450 | 0.3247          | 0.9267   |
  | 0.226         | 1.73  | 1500 | 0.3410          | 0.9267   |
  | 0.1744        | 1.79  | 1550 | 0.3105          | 0.9267   |
  | 0.1943        | 1.85  | 1600 | 0.2760          | 0.94     |
  | 0.2093        | 1.9   | 1650 | 0.2087          | 0.9467   |
  | 0.2027        | 1.96  | 1700 | 0.2773          | 0.9333   |
  | 0.1806        | 2.02  | 1750 | 0.3386          | 0.9267   |
  | 0.1161        | 2.08  | 1800 | 0.4301          | 0.9067   |
  | 0.0916        | 2.13  | 1850 | 0.3693          | 0.92     |
  | 0.1586        | 2.19  | 1900 | 0.2929          | 0.94     |
  | 0.1336        | 2.25  | 1950 | 0.4015          | 0.9133   |
  | 0.1746        | 2.31  | 2000 | 0.3027          | 0.92     |
  | 0.1353        | 2.36  | 2050 | 0.3224          | 0.9267   |
  | 0.116         | 2.42  | 2100 | 0.3609          | 0.9267   |
  | 0.1807        | 2.48  | 2150 | 0.3044          | 0.9267   |
  | 0.1016        | 2.54  | 2200 | 0.3706          | 0.9133   |
  | 0.0634        | 2.6   | 2250 | 0.3391          | 0.92     |
  | 0.167         | 2.65  | 2300 | 0.3463          | 0.92     |
  | 0.1718        | 2.71  | 2350 | 0.3254          | 0.92     |
  | 0.1269        | 2.77  | 2400 | 0.2640          | 0.9333   |
  | 0.1848        | 2.83  | 2450 | 0.2660          | 0.9267   |
  | 0.116         | 2.88  | 2500 | 0.2532          | 0.94     |
  | 0.1804        | 2.94  | 2550 | 0.3538          | 0.92     |
  | 0.1315        | 3.0   | 2600 | 0.4146          | 0.9067   |
  | 0.1024        | 3.06  | 2650 | 0.2899          | 0.9333   |
  | 0.0904        | 3.11  | 2700 | 0.3191          | 0.9333   |
  | 0.0596        | 3.17  | 2750 | 0.3569          | 0.9333   |
  | 0.1144        | 3.23  | 2800 | 0.3373          | 0.9267   |
  | 0.0782        | 3.29  | 2850 | 0.3447          | 0.9267   |
  | 0.064         | 3.34  | 2900 | 0.2932          | 0.94     |
  | 0.118         | 3.4   | 2950 | 0.3099          | 0.94     |
  | 0.1286        | 3.46  | 3000 | 0.3404          | 0.9267   |
  | 0.0963        | 3.52  | 3050 | 0.4026          | 0.9067   |
  | 0.1158        | 3.58  | 3100 | 0.3320          | 0.9267   |
  | 0.0967     