readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  model-index:
  - name: TweetEval_BERT_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        config: sentiment
        split: train
        args: sentiment
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9266666666666666
  ---
  
  <!-- 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. -->
  
  # TweetEval_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5419
  - Accuracy: 0.9267
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6264        | 0.04  | 50   | 0.5266          | 0.74     |
  | 0.5054        | 0.08  | 100  | 0.5959          | 0.6333   |
  | 0.4732        | 0.12  | 150  | 0.3524          | 0.86     |
  | 0.3916        | 0.16  | 200  | 0.3195          | 0.8667   |
  | 0.3477        | 0.2   | 250  | 0.2878          | 0.8867   |
  | 0.3116        | 0.24  | 300  | 0.2903          | 0.92     |
  | 0.3039        | 0.28  | 350  | 0.2488          | 0.8933   |
  | 0.2633        | 0.32  | 400  | 0.2530          | 0.92     |
  | 0.2667        | 0.37  | 450  | 0.2125          | 0.9267   |
  | 0.2604        | 0.41  | 500  | 0.2628          | 0.8867   |
  | 0.278         | 0.45  | 550  | 0.2322          | 0.8867   |
  | 0.2625        | 0.49  | 600  | 0.1903          | 0.92     |
  | 0.2808        | 0.53  | 650  | 0.2400          | 0.8933   |
  | 0.2396        | 0.57  | 700  | 0.2184          | 0.9067   |
  | 0.2571        | 0.61  | 750  | 0.1906          | 0.9133   |
  | 0.2676        | 0.65  | 800  | 0.2467          | 0.9067   |
  | 0.2288        | 0.69  | 850  | 0.2038          | 0.9133   |
  | 0.2959        | 0.73  | 900  | 0.1941          | 0.9      |
  | 0.2619        | 0.77  | 950  | 0.2100          | 0.9333   |
  | 0.2504        | 0.81  | 1000 | 0.1523          | 0.9333   |
  | 0.2338        | 0.85  | 1050 | 0.1429          | 0.94     |
  | 0.2529        | 0.89  | 1100 | 0.1269          | 0.94     |
  | 0.2238        | 0.93  | 1150 | 0.1722          | 0.9333   |
  | 0.2295        | 0.97  | 1200 | 0.1874          | 0.94     |
  | 0.2089        | 1.01  | 1250 | 0.2214          | 0.9067   |
  | 0.1406        | 1.06  | 1300 | 0.3410          | 0.9133   |
  | 0.1587        | 1.1   | 1350 | 0.3330          | 0.9133   |
  | 0.1732        | 1.14  | 1400 | 0.2716          | 0.9133   |
  | 0.195         | 1.18  | 1450 | 0.3726          | 0.92     |
  | 0.1777        | 1.22  | 1500 | 0.2430          | 0.9267   |
  | 0.1433        | 1.26  | 1550 | 0.3011          | 0.9267   |
  | 0.1333        | 1.3   | 1600 | 0.2489          | 0.9333   |
  | 0.1516        | 1.34  | 1650 | 0.3340          | 0.9267   |
  | 0.1774        | 1.38  | 1700 | 0.2497          | 0.8933   |
  | 0.1608        | 1.42  | 1750 | 0.3234          | 0.9      |
  | 0.1534        | 1.46  | 1800 | 0.3383          | 0.9133   |
  | 0.1287        | 1.5   | 1850 | 0.3134          | 0.9133   |
  | 0.1422        | 1.54  | 1900 | 0.3330          | 0.9      |
  | 0.1578        | 1.58  | 1950 | 0.3281          | 0.9133   |
  | 0.1786        | 1.62  | 2000 | 0.2939          | 0.9267   |
  | 0.2019        | 1.66  | 2050 | 0.3535          | 0.9      |
  | 0.1995        | 1.7   | 2100 | 0.3032          | 0.9067   |
  | 0.159         | 1.75  | 2150 | 0.2598          | 0.9267   |
  | 0.1493        | 1.79  | 2200 | 0.2391          | 0.9267   |
  | 0.1748        | 1.83  | 2250 | 0.2258          | 0.92     |
  | 0.1783        | 1.87  | 2300 | 0.2749          | 0.9133   |
  | 0.1619        | 1.91  | 2350 | 0.2699          | 0.92     |
  | 0.1378        | 1.95  | 2400 | 0.2776          | 0.9067   |
  | 0.1529        | 1.99  | 2450 | 0.2235          | 0.9333   |
  | 0.1071        | 2.03  | 2500 | 0.2841          | 0.9267   |
  | 0.0812        | 2.07  | 2550 | 0.3178          | 0.9267   |
  | 0.0464        | 2.11  | 2600 | 0.3567          | 0.92     |
  | 0.1108        | 2.15  | 2650 | 0.2723          | 0.92     |
  | 0.0845        | 2.19  | 2700 | 0.2774          | 0.9267   |
  | 0.0795        | 2.23  | 2750 | 0.3027          | 0.9267   |
  | 0.0403        | 2.27  | 2800 | 0.3566          | 0.9267   |
  | 0.0664        | 2.31  | 2850 | 0.4015          | 0.92     |
  | 0