readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  model-index:
  - name: TweetEval_ALBERT_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_ALBERT_5E
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1990
  - 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4636        | 0.04  | 50   | 0.3662          | 0.8667   |
  | 0.442         | 0.08  | 100  | 0.3471          | 0.84     |
  | 0.3574        | 0.12  | 150  | 0.3446          | 0.86     |
  | 0.392         | 0.16  | 200  | 0.6776          | 0.6267   |
  | 0.4801        | 0.2   | 250  | 0.4307          | 0.7667   |
  | 0.487         | 0.24  | 300  | 0.5127          | 0.8      |
  | 0.4414        | 0.28  | 350  | 0.3912          | 0.8133   |
  | 0.4495        | 0.32  | 400  | 0.4056          | 0.8333   |
  | 0.4637        | 0.37  | 450  | 0.3635          | 0.8533   |
  | 0.4231        | 0.41  | 500  | 0.4235          | 0.84     |
  | 0.4049        | 0.45  | 550  | 0.4094          | 0.8067   |
  | 0.4481        | 0.49  | 600  | 0.3977          | 0.7733   |
  | 0.4024        | 0.53  | 650  | 0.3361          | 0.8733   |
  | 0.3901        | 0.57  | 700  | 0.3014          | 0.8667   |
  | 0.3872        | 0.61  | 750  | 0.3363          | 0.8533   |
  | 0.377         | 0.65  | 800  | 0.3754          | 0.8      |
  | 0.459         | 0.69  | 850  | 0.3861          | 0.8      |
  | 0.437         | 0.73  | 900  | 0.3834          | 0.8333   |
  | 0.3823        | 0.77  | 950  | 0.3541          | 0.8733   |
  | 0.3561        | 0.81  | 1000 | 0.3177          | 0.84     |
  | 0.4536        | 0.85  | 1050 | 0.4291          | 0.78     |
  | 0.4457        | 0.89  | 1100 | 0.3193          | 0.86     |
  | 0.3478        | 0.93  | 1150 | 0.3159          | 0.8533   |
  | 0.4613        | 0.97  | 1200 | 0.3605          | 0.84     |
  | 0.4081        | 1.01  | 1250 | 0.4291          | 0.7867   |
  | 0.3849        | 1.06  | 1300 | 0.3114          | 0.8733   |
  | 0.4071        | 1.1   | 1350 | 0.2939          | 0.8667   |
  | 0.3484        | 1.14  | 1400 | 0.3212          | 0.84     |
  | 0.3869        | 1.18  | 1450 | 0.2717          | 0.8933   |
  | 0.3877        | 1.22  | 1500 | 0.3459          | 0.84     |
  | 0.4245        | 1.26  | 1550 | 0.3404          | 0.8733   |
  | 0.4148        | 1.3   | 1600 | 0.2863          | 0.8667   |
  | 0.3542        | 1.34  | 1650 | 0.3377          | 0.86     |
  | 0.4093        | 1.38  | 1700 | 0.2972          | 0.8867   |
  | 0.3579        | 1.42  | 1750 | 0.3926          | 0.86     |
  | 0.3892        | 1.46  | 1800 | 0.2870          | 0.8667   |
  | 0.3569        | 1.5   | 1850 | 0.4027          | 0.8467   |
  | 0.3493        | 1.54  | 1900 | 0.3069          | 0.8467   |
  | 0.36          | 1.58  | 1950 | 0.3197          | 0.8733   |
  | 0.3532        | 1.62  | 2000 | 0.3711          | 0.8667   |
  | 0.3311        | 1.66  | 2050 | 0.2897          | 0.8867   |
  | 0.346         | 1.7   | 2100 | 0.2938          | 0.88     |
  | 0.3389        | 1.75  | 2150 | 0.2734          | 0.8933   |
  | 0.3289        | 1.79  | 2200 | 0.2606          | 0.8867   |
  | 0.3558        | 1.83  | 2250 | 0.3070          | 0.88     |
  | 0.3277        | 1.87  | 2300 | 0.2757          | 0.8867   |
  | 0.3166        | 1.91  | 2350 | 0.2759          | 0.8733   |
  | 0.3223        | 1.95  | 2400 | 0.2053          | 0.9133   |
  | 0.317         | 1.99  | 2450 | 0.2307          | 0.8867   |
  | 0.3408        | 2.03  | 2500 | 0.2557          | 0.9067   |
  | 0.3212        | 2.07  | 2550 | 0.2508          | 0.8867   |
  | 0.2806        | 2.11  | 2600 | 0.2472          | 0.88     |
  | 0.3567        | 2.15  | 2650 | 0.2790          | 0.8933   |
  | 0.2887        | 2.19  | 2700 | 0.3197          | 0.88     |
  | 0.3222        | 2.23  | 2750 | 0.2943          | 0.8667   |
  | 0.2773        | 2.27  | 2800 | 0.2297          | 0.88     |
  | 0.2728        | 2.31  | 2850 | 0.2813          | 0.8733   |
  | 