readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_DistilBERT_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_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3301
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6534        | 0.06  | 50   | 0.5269          | 0.8333   |
  | 0.3926        | 0.12  | 100  | 0.2674          | 0.9133   |
  | 0.275         | 0.17  | 150  | 0.2063          | 0.94     |
  | 0.2341        | 0.23  | 200  | 0.1896          | 0.9333   |
  | 0.2436        | 0.29  | 250  | 0.2132          | 0.9133   |
  | 0.2561        | 0.35  | 300  | 0.2474          | 0.9      |
  | 0.2536        | 0.4   | 350  | 0.2092          | 0.9267   |
  | 0.2048        | 0.46  | 400  | 0.2135          | 0.92     |
  | 0.2119        | 0.52  | 450  | 0.2382          | 0.9133   |
  | 0.2152        | 0.58  | 500  | 0.2322          | 0.9267   |
  | 0.2072        | 0.63  | 550  | 0.2182          | 0.9333   |
  | 0.2134        | 0.69  | 600  | 0.2457          | 0.9133   |
  | 0.2093        | 0.75  | 650  | 0.2476          | 0.92     |
  | 0.2145        | 0.81  | 700  | 0.2489          | 0.9267   |
  | 0.2191        | 0.87  | 750  | 0.2374          | 0.9267   |
  | 0.2198        | 0.92  | 800  | 0.2347          | 0.92     |
  | 0.2126        | 0.98  | 850  | 0.2015          | 0.9467   |
  | 0.1373        | 1.04  | 900  | 0.2246          | 0.9467   |
  | 0.1367        | 1.1   | 950  | 0.2875          | 0.9133   |
  | 0.1726        | 1.15  | 1000 | 0.2641          | 0.94     |
  | 0.1968        | 1.21  | 1050 | 0.2653          | 0.9333   |
  | 0.1607        | 1.27  | 1100 | 0.2323          | 0.94     |
  | 0.1437        | 1.33  | 1150 | 0.2900          | 0.9267   |
  | 0.1707        | 1.38  | 1200 | 0.2430          | 0.94     |
  | 0.1174        | 1.44  | 1250 | 0.2553          | 0.94     |
  | 0.1662        | 1.5   | 1300 | 0.2442          | 0.9467   |
  | 0.1374        | 1.56  | 1350 | 0.2365          | 0.9467   |
  | 0.1632        | 1.61  | 1400 | 0.2794          | 0.9133   |
  | 0.1558        | 1.67  | 1450 | 0.2428          | 0.94     |
  | 0.1717        | 1.73  | 1500 | 0.2380          | 0.92     |
  | 0.1301        | 1.79  | 1550 | 0.2006          | 0.94     |
  | 0.1757        | 1.85  | 1600 | 0.2327          | 0.9467   |
  | 0.1997        | 1.9   | 1650 | 0.2160          | 0.94     |
  | 0.1611        | 1.96  | 1700 | 0.2797          | 0.92     |
  | 0.1638        | 2.02  | 1750 | 0.2433          | 0.9333   |
  | 0.1041        | 2.08  | 1800 | 0.2389          | 0.94     |
  | 0.1172        | 2.13  | 1850 | 0.2381          | 0.9467   |
  | 0.1332        | 2.19  | 1900 | 0.2650          | 0.94     |
  | 0.1299        | 2.25  | 1950 | 0.2869          | 0.9333   |
  | 0.0992        | 2.31  | 2000 | 0.2308          | 0.9533   |
  | 0.1012        | 2.36  | 2050 | 0.2552          | 0.9467   |
  | 0.0948        | 2.42  | 2100 | 0.2823          | 0.9267   |
  | 0.1081        | 2.48  | 2150 | 0.2634          | 0.9467   |
  | 0.1157        | 2.54  | 2200 | 0.2864          | 0.9333   |
  | 0.1154        | 2.6   | 2250 | 0.2987          | 0.9267   |
  | 0.1259        | 2.65  | 2300 | 0.2879          | 0.9333   |
  | 0.1084        | 2.71  | 2350 | 0.2661          | 0.94     |
  | 0.1342        | 2.77  | 2400 | 0.2711          | 0.94     |
  | 0.12          | 2.83  | 2450 | 0.2362          | 0.9467   |
  | 0.0839        | 2.88  | 2500 | 0.2712          | 0.9333   |
  | 0.1546        | 2.94  | 2550 | 0.2433          | 0.9467   |
  | 0.1321        | 3.0   | 2600 | 0.2421          | 0.9467   |
  | 0.101         | 3.06  | 2650 | 0.2820          | 0.9333   |
  | 0.061         | 3.11  | 2700 | 0.2990          | 0.9267   |
  | 0.0608        | 3.17  | 2750 | 0.2512          | 0.9467   |
  | 0.0983        | 3.23  | 2800 | 0.3033          | 0.9333   |
  | 0.0806        | 3.29  | 2850 | 0.2621          | 0.9467   |
  | 0.0788        | 3.34  | 2900 | 0.2672          | 0.9467   |
  | 0.0827        | 3.4   | 2950 | 0.2797          | 0.9467   |
  | 0.0912        | 3.46  | 3000 | 0.2802          | 0.9467   |
  | 0.0771        | 3.52  | 3050 | 0.2693          | 0.9467   |
  | 0.0842        | 3.58  | 3100 | 0.2758          