readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_roBERTa_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: 0.8933333333333333
  ---
  
  <!-- 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_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4796
  - Accuracy: 0.8933
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.699         | 0.08  | 50   | 0.6734          | 0.5467   |
  | 0.6099        | 0.16  | 100  | 0.4322          | 0.8      |
  | 0.4906        | 0.24  | 150  | 0.3861          | 0.84     |
  | 0.4652        | 0.32  | 200  | 0.4288          | 0.7933   |
  | 0.4874        | 0.4   | 250  | 0.3872          | 0.84     |
  | 0.4735        | 0.48  | 300  | 0.3401          | 0.8667   |
  | 0.3909        | 0.56  | 350  | 0.3484          | 0.84     |
  | 0.4277        | 0.64  | 400  | 0.3207          | 0.88     |
  | 0.3894        | 0.72  | 450  | 0.3310          | 0.8733   |
  | 0.4523        | 0.8   | 500  | 0.3389          | 0.8667   |
  | 0.4087        | 0.88  | 550  | 0.3515          | 0.8467   |
  | 0.3973        | 0.96  | 600  | 0.3513          | 0.8467   |
  | 0.4016        | 1.04  | 650  | 0.3501          | 0.8667   |
  | 0.3613        | 1.12  | 700  | 0.3327          | 0.8667   |
  | 0.343         | 1.2   | 750  | 0.3518          | 0.86     |
  | 0.314         | 1.28  | 800  | 0.3555          | 0.88     |
  | 0.3407        | 1.36  | 850  | 0.3849          | 0.86     |
  | 0.2944        | 1.44  | 900  | 0.3576          | 0.8667   |
  | 0.3267        | 1.52  | 950  | 0.3461          | 0.8733   |
  | 0.3251        | 1.6   | 1000 | 0.3411          | 0.8667   |
  | 0.321         | 1.68  | 1050 | 0.3371          | 0.88     |
  | 0.3057        | 1.76  | 1100 | 0.3322          | 0.88     |
  | 0.3335        | 1.84  | 1150 | 0.3106          | 0.8667   |
  | 0.3363        | 1.92  | 1200 | 0.3158          | 0.8933   |
  | 0.2972        | 2.0   | 1250 | 0.3122          | 0.88     |
  | 0.2453        | 2.08  | 1300 | 0.3327          | 0.8867   |
  | 0.2467        | 2.16  | 1350 | 0.3767          | 0.8667   |
  | 0.273         | 2.24  | 1400 | 0.3549          | 0.8667   |
  | 0.2672        | 2.32  | 1450 | 0.3470          | 0.88     |
  | 0.2352        | 2.4   | 1500 | 0.4092          | 0.8667   |
  | 0.2763        | 2.48  | 1550 | 0.3472          | 0.9      |
  | 0.2858        | 2.56  | 1600 | 0.3440          | 0.9      |
  | 0.2206        | 2.64  | 1650 | 0.3770          | 0.88     |
  | 0.2928        | 2.72  | 1700 | 0.3280          | 0.8867   |
  | 0.2478        | 2.8   | 1750 | 0.3426          | 0.8867   |
  | 0.2362        | 2.88  | 1800 | 0.3578          | 0.8933   |
  | 0.2107        | 2.96  | 1850 | 0.3986          | 0.8933   |
  | 0.2191        | 3.04  | 1900 | 0.3819          | 0.8933   |
  | 0.2267        | 3.12  | 1950 | 0.4047          | 0.8867   |
  | 0.2076        | 3.2   | 2000 | 0.4303          | 0.8867   |
  | 0.1868        | 3.28  | 2050 | 0.4385          | 0.8933   |
  | 0.2239        | 3.36  | 2100 | 0.4175          | 0.8933   |
  | 0.2082        | 3.44  | 2150 | 0.4142          | 0.8933   |
  | 0.2423        | 3.52  | 2200 | 0.4002          | 0.8867   |
  | 0.1878        | 3.6   | 2250 | 0.4662          | 0.88     |
  | 0.1892        | 3.68  | 2300 | 0.4783          | 0.88     |
  | 0.2259        | 3.76  | 2350 | 0.4487          | 0.88     |
  | 0.1859        | 3.84  | 2400 | 0.4456          | 0.8933   |
  | 0.2042        | 3.92  | 2450 | 0.4468          | 0.8933   |
  | 0.2096        | 4.0   | 2500 | 0.4153          | 0.8867   |
  | 0.178         | 4.08  | 2550 | 0.4100          | 0.8933   |
  | 0.1621        | 4.16  | 2600 | 0.4292          | 0.8933   |
  | 0.1682        | 4.24  | 2650 | 0.4602          | 0.8933   |
  | 0.1813        | 4.32  | 2700 | 0.4680          | 0.8933   |
  | 0.2033        | 4.4   | 2750 | 0.4735          | 0.8933   |
  | 0.1662        | 4.48  | 2800 | 0.4750          | 0.88     |
  | 0.1686        | 4.56  | 2850 | 0.4830          | 0.