readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_BERT_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: 0.82
  ---
  
  <!-- 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_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7061
  - Accuracy: 0.82
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6882        | 0.08  | 50   | 0.6047          | 0.7      |
  | 0.6223        | 0.16  | 100  | 0.5137          | 0.8067   |
  | 0.5463        | 0.24  | 150  | 0.4573          | 0.8067   |
  | 0.4922        | 0.32  | 200  | 0.4790          | 0.8      |
  | 0.4821        | 0.4   | 250  | 0.4207          | 0.8267   |
  | 0.4985        | 0.48  | 300  | 0.4267          | 0.8067   |
  | 0.4455        | 0.56  | 350  | 0.4301          | 0.8133   |
  | 0.469         | 0.64  | 400  | 0.4294          | 0.82     |
  | 0.4906        | 0.72  | 450  | 0.4059          | 0.8067   |
  | 0.4006        | 0.8   | 500  | 0.4181          | 0.8133   |
  | 0.445         | 0.88  | 550  | 0.3948          | 0.8267   |
  | 0.4302        | 0.96  | 600  | 0.3976          | 0.84     |
  | 0.4442        | 1.04  | 650  | 0.3887          | 0.8533   |
  | 0.3424        | 1.12  | 700  | 0.4119          | 0.8267   |
  | 0.3589        | 1.2   | 750  | 0.4083          | 0.8533   |
  | 0.3737        | 1.28  | 800  | 0.4253          | 0.8333   |
  | 0.334         | 1.36  | 850  | 0.4147          | 0.86     |
  | 0.3637        | 1.44  | 900  | 0.3926          | 0.8533   |
  | 0.3388        | 1.52  | 950  | 0.4084          | 0.8267   |
  | 0.3375        | 1.6   | 1000 | 0.4132          | 0.8467   |
  | 0.3725        | 1.68  | 1050 | 0.3965          | 0.8467   |
  | 0.3649        | 1.76  | 1100 | 0.3956          | 0.8333   |
  | 0.3799        | 1.84  | 1150 | 0.3923          | 0.8333   |
  | 0.3695        | 1.92  | 1200 | 0.4266          | 0.84     |
  | 0.3233        | 2.0   | 1250 | 0.4225          | 0.8333   |
  | 0.2313        | 2.08  | 1300 | 0.4672          | 0.8333   |
  | 0.231         | 2.16  | 1350 | 0.5212          | 0.8133   |
  | 0.2526        | 2.24  | 1400 | 0.5392          | 0.8067   |
  | 0.2721        | 2.32  | 1450 | 0.4895          | 0.82     |
  | 0.2141        | 2.4   | 1500 | 0.5258          | 0.8133   |
  | 0.2658        | 2.48  | 1550 | 0.5046          | 0.8267   |
  | 0.2386        | 2.56  | 1600 | 0.4873          | 0.8267   |
  | 0.2493        | 2.64  | 1650 | 0.4950          | 0.8333   |
  | 0.2692        | 2.72  | 1700 | 0.5080          | 0.8267   |
  | 0.2226        | 2.8   | 1750 | 0.5016          | 0.8467   |
  | 0.2522        | 2.88  | 1800 | 0.5068          | 0.8267   |
  | 0.2556        | 2.96  | 1850 | 0.4937          | 0.8267   |
  | 0.2311        | 3.04  | 1900 | 0.5103          | 0.8267   |
  | 0.1703        | 3.12  | 1950 | 0.5680          | 0.82     |
  | 0.1744        | 3.2   | 2000 | 0.5501          | 0.82     |
  | 0.1667        | 3.28  | 2050 | 0.6142          | 0.82     |
  | 0.1863        | 3.36  | 2100 | 0.6355          | 0.82     |
  | 0.2543        | 3.44  | 2150 | 0.6000          | 0.8133   |
  | 0.1565        | 3.52  | 2200 | 0.6618          | 0.8267   |
  | 0.1531        | 3.6   | 2250 | 0.6595          | 0.8133   |
  | 0.1915        | 3.68  | 2300 | 0.6647          | 0.8267   |
  | 0.1601        | 3.76  | 2350 | 0.6729          | 0.8267   |
  | 0.176         | 3.84  | 2400 | 0.6699          | 0.82     |
  | 0.1815        | 3.92  | 2450 | 0.6819          | 0.8067   |
  | 0.1987        | 4.0   | 2500 | 0.6543          | 0.8333   |
  | 0.1236        | 4.08  | 2550 | 0.6686          | 0.8333   |
  | 0.1599        | 4.16  | 2600 | 0.6583          | 0.8267   |
  | 0.1256        | 4.24  | 2650 | 0.6871          | 0.8267   |
  | 0.1291        | 4.32  | 2700 | 0.6855          | 0.82     |
  | 0.1198        | 4.4   | 2750 | 0.6901          | 0.82     |
  | 0.1245        | 4.48  | 2800 | 0.7152          | 0.8267   |
  | 0.1784        | 4.56  | 2850 | 0.7053          | 0.82     |
  