readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_ELECTRA_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: sentiment140
        type: sentiment140
        config: sentiment140
        split: train
        args: sentiment140
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.84
  ---
  
  <!-- 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. -->
  
  # Sentiment140_ELECTRA_5E
  
  This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5410
  - Accuracy: 0.84
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6896        | 0.08  | 50   | 0.6605          | 0.7133   |
  | 0.6664        | 0.16  | 100  | 0.6054          | 0.7133   |
  | 0.5915        | 0.24  | 150  | 0.4777          | 0.8333   |
  | 0.5053        | 0.32  | 200  | 0.4735          | 0.7733   |
  | 0.4946        | 0.4   | 250  | 0.3847          | 0.8267   |
  | 0.4578        | 0.48  | 300  | 0.4025          | 0.8067   |
  | 0.4724        | 0.56  | 350  | 0.3642          | 0.8333   |
  | 0.4309        | 0.64  | 400  | 0.3762          | 0.86     |
  | 0.4818        | 0.72  | 450  | 0.3829          | 0.84     |
  | 0.416         | 0.8   | 500  | 0.3599          | 0.8467   |
  | 0.4201        | 0.88  | 550  | 0.3469          | 0.8533   |
  | 0.3664        | 0.96  | 600  | 0.3462          | 0.8467   |
  | 0.4289        | 1.04  | 650  | 0.3470          | 0.86     |
  | 0.3859        | 1.12  | 700  | 0.3440          | 0.8533   |
  | 0.3599        | 1.2   | 750  | 0.3475          | 0.8533   |
  | 0.3287        | 1.28  | 800  | 0.3524          | 0.8467   |
  | 0.3331        | 1.36  | 850  | 0.3475          | 0.8733   |
  | 0.3236        | 1.44  | 900  | 0.3657          | 0.8467   |
  | 0.3502        | 1.52  | 950  | 0.3525          | 0.84     |
  | 0.3702        | 1.6   | 1000 | 0.3655          | 0.8333   |
  | 0.3323        | 1.68  | 1050 | 0.3405          | 0.84     |
  | 0.3452        | 1.76  | 1100 | 0.3376          | 0.8533   |
  | 0.3742        | 1.84  | 1150 | 0.3481          | 0.8533   |
  | 0.3145        | 1.92  | 1200 | 0.3472          | 0.86     |
  | 0.3657        | 2.0   | 1250 | 0.3302          | 0.8733   |
  | 0.2601        | 2.08  | 1300 | 0.3612          | 0.86     |
  | 0.2954        | 2.16  | 1350 | 0.3640          | 0.8533   |
  | 0.2888        | 2.24  | 1400 | 0.3670          | 0.8467   |
  | 0.2572        | 2.32  | 1450 | 0.4118          | 0.84     |
  | 0.2955        | 2.4   | 1500 | 0.3811          | 0.86     |
  | 0.2431        | 2.48  | 1550 | 0.4221          | 0.84     |
  | 0.318         | 2.56  | 1600 | 0.3844          | 0.8467   |
  | 0.2615        | 2.64  | 1650 | 0.4109          | 0.8333   |
  | 0.2389        | 2.72  | 1700 | 0.4420          | 0.8467   |
  | 0.2983        | 2.8   | 1750 | 0.4203          | 0.8467   |
  | 0.2828        | 2.88  | 1800 | 0.3629          | 0.8733   |
  | 0.2897        | 2.96  | 1850 | 0.3916          | 0.8733   |
  | 0.2239        | 3.04  | 1900 | 0.4143          | 0.86     |
  | 0.2093        | 3.12  | 1950 | 0.4521          | 0.84     |
  | 0.2438        | 3.2   | 2000 | 0.4271          | 0.8467   |
  | 0.2282        | 3.28  | 2050 | 0.4548          | 0.8333   |
  | 0.1918        | 3.36  | 2100 | 0.4533          | 0.86     |
  | 0.1698        | 3.44  | 2150 | 0.5177          | 0.84     |
  | 0.2765        | 3.52  | 2200 | 0.4884          | 0.84     |
  | 0.2282        | 3.6   | 2250 | 0.4697          | 0.8533   |
  | 0.239         | 3.68  | 2300 | 0.4766          | 0.8533   |
  | 0.2219        | 3.76  | 2350 | 0.4628          | 0.8533   |
  | 0.2375        | 3.84  | 2400 | 0.4704          | 0.8533   |
  | 0.1883        | 3.92  | 2450 | 0.4744          | 0.84     |
  | 0.2049        | 4.0   | 2500 | 0.4977          | 0.84     |
  | 0.1958        | 4.08  | 2550 | 0.4906          | 0.84     |
  | 0.1656        | 4.16  | 2600 | 0.5219          | 0.8333   |
  | 0.1543        | 4.24  | 2650 | 0.5379          | 0.8333   |
  | 0.2082        | 4.32  | 2700 | 0.5107          | 0.84     |
  | 0.1724        | 4.4   | 2750 | 0.5208          | 0.84     |
  | 0.1778        | 4.48  | 2800 | 0.5238          | 0.84     |
  | 0.1914        | 4.