readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  model-index:
  - name: TweetEval_XLNET_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: 0.9333333333333333
  ---
  
  <!-- 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_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4591
  - 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5575        | 0.04  | 50   | 0.2675          | 0.9      |
  | 0.4177        | 0.08  | 100  | 0.2193          | 0.9067   |
  | 0.2911        | 0.12  | 150  | 0.2482          | 0.9      |
  | 0.3503        | 0.16  | 200  | 0.2424          | 0.9      |
  | 0.3412        | 0.2   | 250  | 0.1913          | 0.9267   |
  | 0.2747        | 0.24  | 300  | 0.1783          | 0.92     |
  | 0.2999        | 0.28  | 350  | 0.2495          | 0.9133   |
  | 0.3141        | 0.32  | 400  | 0.2460          | 0.9      |
  | 0.2935        | 0.37  | 450  | 0.2034          | 0.92     |
  | 0.2619        | 0.41  | 500  | 0.2600          | 0.9067   |
  | 0.2454        | 0.45  | 550  | 0.2178          | 0.92     |
  | 0.2809        | 0.49  | 600  | 0.2254          | 0.9133   |
  | 0.288         | 0.53  | 650  | 0.1849          | 0.92     |
  | 0.2769        | 0.57  | 700  | 0.1896          | 0.9267   |
  | 0.3079        | 0.61  | 750  | 0.2153          | 0.9133   |
  | 0.2598        | 0.65  | 800  | 0.3279          | 0.9067   |
  | 0.3149        | 0.69  | 850  | 0.1985          | 0.92     |
  | 0.2872        | 0.73  | 900  | 0.1801          | 0.9333   |
  | 0.2554        | 0.77  | 950  | 0.2023          | 0.9267   |
  | 0.2645        | 0.81  | 1000 | 0.2208          | 0.9067   |
  | 0.2509        | 0.85  | 1050 | 0.2012          | 0.9333   |
  | 0.2404        | 0.89  | 1100 | 0.1995          | 0.9067   |
  | 0.2361        | 0.93  | 1150 | 0.1808          | 0.9133   |
  | 0.2298        | 0.97  | 1200 | 0.2226          | 0.9333   |
  | 0.193         | 1.01  | 1250 | 0.2535          | 0.9267   |
  | 0.1603        | 1.06  | 1300 | 0.2163          | 0.9467   |
  | 0.1916        | 1.1   | 1350 | 0.2479          | 0.92     |
  | 0.1963        | 1.14  | 1400 | 0.1964          | 0.94     |
  | 0.1667        | 1.18  | 1450 | 0.3139          | 0.9133   |
  | 0.1668        | 1.22  | 1500 | 0.2204          | 0.9267   |
  | 0.1677        | 1.26  | 1550 | 0.2468          | 0.9333   |
  | 0.1601        | 1.3   | 1600 | 0.2394          | 0.94     |
  | 0.1714        | 1.34  | 1650 | 0.2326          | 0.94     |
  | 0.197         | 1.38  | 1700 | 0.1861          | 0.94     |
  | 0.1777        | 1.42  | 1750 | 0.2518          | 0.94     |
  | 0.1925        | 1.46  | 1800 | 0.1806          | 0.94     |
  | 0.2068        | 1.5   | 1850 | 0.1319          | 0.9467   |
  | 0.1716        | 1.54  | 1900 | 0.1199          | 0.9667   |
  | 0.1442        | 1.58  | 1950 | 0.1694          | 0.96     |
  | 0.1929        | 1.62  | 2000 | 0.1990          | 0.9467   |
  | 0.1654        | 1.66  | 2050 | 0.2972          | 0.9333   |
  | 0.1759        | 1.7   | 2100 | 0.1584          | 0.9467   |
  | 0.1788        | 1.75  | 2150 | 0.2266          | 0.94     |
  | 0.1796        | 1.79  | 2200 | 0.2746          | 0.9333   |
  | 0.172         | 1.83  | 2250 | 0.2313          | 0.9333   |
  | 0.1637        | 1.87  | 2300 | 0.2918          | 0.9267   |
  | 0.2359        | 1.91  | 2350 | 0.2121          | 0.9267   |
  | 0.1778        | 1.95  | 2400 | 0.2022          | 0.9333   |
  | 0.1581        | 1.99  | 2450 | 0.2936          | 0.9067   |
  | 0.1312        | 2.03  | 2500 | 0.2531          | 0.9333   |
  | 0.1178        | 2.07  | 2550 | 0.2525          | 0.9267   |
  | 0.0924        | 2.11  | 2600 | 0.2715          | 0.9333   |
  | 0.0774        | 2.15  | 2650 | 0.2123          | 0.9533   |
  | 0.091         | 2.19  | 2700 | 0.2128          | 0.9467   |
  | 0.0948        | 2.23  | 2750 | 0.2187          | 0.9533   |
  | 0.1121        | 2.27  | 2800 | 0.2438          | 0.9467   |
  | 0.1259        | 2.31  | 2850 | 0.2197          | 0.9467   |
  | 0.074