readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_DistilBERT_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: 0.8333333333333334
  ---
  
  <!-- 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_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4897
  - Accuracy: 0.8333
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6784        | 0.08  | 50   | 0.6516          | 0.6933   |
  | 0.6301        | 0.16  | 100  | 0.5384          | 0.7533   |
  | 0.5438        | 0.24  | 150  | 0.4559          | 0.8      |
  | 0.4625        | 0.32  | 200  | 0.4287          | 0.8133   |
  | 0.4528        | 0.4   | 250  | 0.4056          | 0.8267   |
  | 0.4609        | 0.48  | 300  | 0.3883          | 0.8333   |
  | 0.4705        | 0.56  | 350  | 0.3886          | 0.8067   |
  | 0.4539        | 0.64  | 400  | 0.3967          | 0.82     |
  | 0.4483        | 0.72  | 450  | 0.3758          | 0.82     |
  | 0.4699        | 0.8   | 500  | 0.4003          | 0.8133   |
  | 0.467         | 0.88  | 550  | 0.4021          | 0.8267   |
  | 0.454         | 0.96  | 600  | 0.3735          | 0.8333   |
  | 0.4227        | 1.04  | 650  | 0.3840          | 0.8267   |
  | 0.3584        | 1.12  | 700  | 0.3775          | 0.8333   |
  | 0.3618        | 1.2   | 750  | 0.4026          | 0.8267   |
  | 0.3634        | 1.28  | 800  | 0.3891          | 0.8133   |
  | 0.3751        | 1.36  | 850  | 0.3895          | 0.8267   |
  | 0.3484        | 1.44  | 900  | 0.3919          | 0.8267   |
  | 0.3764        | 1.52  | 950  | 0.3770          | 0.84     |
  | 0.3488        | 1.6   | 1000 | 0.4028          | 0.82     |
  | 0.3665        | 1.68  | 1050 | 0.3779          | 0.8333   |
  | 0.3925        | 1.76  | 1100 | 0.3726          | 0.84     |
  | 0.3624        | 1.84  | 1150 | 0.3655          | 0.84     |
  | 0.3876        | 1.92  | 1200 | 0.3648          | 0.8133   |
  | 0.3935        | 2.0   | 1250 | 0.3633          | 0.8467   |
  | 0.2944        | 2.08  | 1300 | 0.3808          | 0.8333   |
  | 0.2957        | 2.16  | 1350 | 0.3836          | 0.8333   |
  | 0.266         | 2.24  | 1400 | 0.3940          | 0.8267   |
  | 0.2747        | 2.32  | 1450 | 0.3952          | 0.84     |
  | 0.314         | 2.4   | 1500 | 0.4060          | 0.8133   |
  | 0.3419        | 2.48  | 1550 | 0.4025          | 0.8133   |
  | 0.2782        | 2.56  | 1600 | 0.4218          | 0.82     |
  | 0.3218        | 2.64  | 1650 | 0.4039          | 0.8333   |
  | 0.2863        | 2.72  | 1700 | 0.4130          | 0.8267   |
  | 0.3336        | 2.8   | 1750 | 0.4026          | 0.8133   |
  | 0.3224        | 2.88  | 1800 | 0.3910          | 0.8267   |
  | 0.2709        | 2.96  | 1850 | 0.3979          | 0.84     |
  | 0.2701        | 3.04  | 1900 | 0.4127          | 0.8333   |
  | 0.2782        | 3.12  | 1950 | 0.4335          | 0.82     |
  | 0.2425        | 3.2   | 2000 | 0.4229          | 0.8333   |
  | 0.2457        | 3.28  | 2050 | 0.4168          | 0.8333   |
  | 0.217         | 3.36  | 2100 | 0.4264          | 0.8267   |
  | 0.2522        | 3.44  | 2150 | 0.4250          | 0.8333   |
  | 0.2402        | 3.52  | 2200 | 0.4371          | 0.8333   |
  | 0.2465        | 3.6   | 2250 | 0.4429          | 0.8333   |
  | 0.2427        | 3.68  | 2300 | 0.4435          | 0.8333   |
  | 0.2408        | 3.76  | 2350 | 0.4500          | 0.84     |
  | 0.1976        | 3.84  | 2400 | 0.4536          | 0.8333   |
  | 0.23          | 3.92  | 2450 | 0.4645          | 0.8333   |
  | 0.2449        | 4.0   | 2500 | 0.4557          | 0.8467   |
  | 0.1933        | 4.08  | 2550 | 0.4672          | 0.84     |
  | 0.213         | 4.16  | 2600 | 0.4717          | 0.84     |
  | 0.1772        | 4.24  | 2650 | 0.4843          | 0.8267   |
  | 0.1917        | 4.32  | 2700 | 0.4690          | 0.8467   |
  | 0.2094        | 4.4   | 2750 | 0.4728          | 0.8467   |
  | 0.1903        | 4.48  | 2800 | 0.4755          | 0.8467   |
  | 0.2541        | 