readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  model-index:
  - name: TweetEval_DistilBERT_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: 0.9133333333333333
  ---
  
  <!-- 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_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4043
  - Accuracy: 0.9133
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5747        | 0.04  | 50   | 0.4843          | 0.7333   |
  | 0.4336        | 0.08  | 100  | 0.2888          | 0.8667   |
  | 0.3437        | 0.12  | 150  | 0.2895          | 0.8667   |
  | 0.3375        | 0.16  | 200  | 0.2864          | 0.8733   |
  | 0.3072        | 0.2   | 250  | 0.2577          | 0.8867   |
  | 0.3019        | 0.24  | 300  | 0.2574          | 0.8933   |
  | 0.2662        | 0.28  | 350  | 0.2621          | 0.8867   |
  | 0.283         | 0.32  | 400  | 0.2340          | 0.92     |
  | 0.2949        | 0.37  | 450  | 0.2482          | 0.8933   |
  | 0.3066        | 0.41  | 500  | 0.2537          | 0.9      |
  | 0.2457        | 0.45  | 550  | 0.2473          | 0.9      |
  | 0.295         | 0.49  | 600  | 0.2177          | 0.9133   |
  | 0.2862        | 0.53  | 650  | 0.2215          | 0.9133   |
  | 0.2603        | 0.57  | 700  | 0.2272          | 0.9133   |
  | 0.2976        | 0.61  | 750  | 0.2298          | 0.9067   |
  | 0.2823        | 0.65  | 800  | 0.2451          | 0.8933   |
  | 0.2583        | 0.69  | 850  | 0.2645          | 0.8933   |
  | 0.2694        | 0.73  | 900  | 0.2352          | 0.9      |
  | 0.2433        | 0.77  | 950  | 0.2322          | 0.9133   |
  | 0.2598        | 0.81  | 1000 | 0.2300          | 0.9      |
  | 0.2701        | 0.85  | 1050 | 0.2162          | 0.9      |
  | 0.2227        | 0.89  | 1100 | 0.2135          | 0.8933   |
  | 0.2045        | 0.93  | 1150 | 0.2233          | 0.9133   |
  | 0.2821        | 0.97  | 1200 | 0.2194          | 0.9      |
  | 0.2342        | 1.01  | 1250 | 0.2488          | 0.88     |
  | 0.2028        | 1.06  | 1300 | 0.2451          | 0.8867   |
  | 0.1509        | 1.1   | 1350 | 0.3174          | 0.88     |
  | 0.1888        | 1.14  | 1400 | 0.2537          | 0.9133   |
  | 0.1825        | 1.18  | 1450 | 0.2559          | 0.9067   |
  | 0.1721        | 1.22  | 1500 | 0.2511          | 0.92     |
  | 0.2137        | 1.26  | 1550 | 0.2963          | 0.9133   |
  | 0.2153        | 1.3   | 1600 | 0.2210          | 0.92     |
  | 0.1989        | 1.34  | 1650 | 0.2231          | 0.9133   |
  | 0.2155        | 1.38  | 1700 | 0.1991          | 0.9133   |
  | 0.1912        | 1.42  | 1750 | 0.2146          | 0.92     |
  | 0.1623        | 1.46  | 1800 | 0.2721          | 0.9      |
  | 0.2236        | 1.5   | 1850 | 0.2301          | 0.9267   |
  | 0.1907        | 1.54  | 1900 | 0.1988          | 0.92     |
  | 0.1286        | 1.58  | 1950 | 0.2326          | 0.9      |
  | 0.2147        | 1.62  | 2000 | 0.2432          | 0.9267   |
  | 0.2018        | 1.66  | 2050 | 0.2162          | 0.9067   |
  | 0.2073        | 1.7   | 2100 | 0.2153          | 0.9133   |
  | 0.1498        | 1.75  | 2150 | 0.2335          | 0.92     |
  | 0.1812        | 1.79  | 2200 | 0.2275          | 0.9267   |
  | 0.1482        | 1.83  | 2250 | 0.2734          | 0.9      |
  | 0.2233        | 1.87  | 2300 | 0.2454          | 0.9      |
  | 0.1673        | 1.91  | 2350 | 0.2394          | 0.92     |
  | 0.1555        | 1.95  | 2400 | 0.2725          | 0.92     |
  | 0.2082        | 1.99  | 2450 | 0.2684          | 0.9133   |
  | 0.1545        | 2.03  | 2500 | 0.3049          | 0.9067   |
  | 0.1384        | 2.07  | 2550 | 0.2960          | 0.9133   |
  | 0.1201        | 2.11  | 2600 | 0.3259          | 0.9      |
  | 0.1348        | 2.15  | 2650 | 0.3091          | 0.9133   |
  | 0.1046        | 2.19  | 2700 | 0.2916          | 0.9267   |
  | 0.1506        | 2.23  | 2750 | 0.2910          | 0.9133   |
  | 0.1481        | 2.27  | 2800 | 0.2855          | 0.9067   |
  | 0.1318        | 2.31  | 2850 | 0.3075   