readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_ALBERT_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: 0.8533333333333334
  ---
  
  <!-- 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_ALBERT_5E
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6103
  - Accuracy: 0.8533
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6713        | 0.08  | 50   | 0.5704          | 0.7333   |
  | 0.5742        | 0.16  | 100  | 0.4620          | 0.8      |
  | 0.5104        | 0.24  | 150  | 0.5536          | 0.74     |
  | 0.5313        | 0.32  | 200  | 0.5198          | 0.76     |
  | 0.5023        | 0.4   | 250  | 0.4286          | 0.8      |
  | 0.4871        | 0.48  | 300  | 0.4294          | 0.8267   |
  | 0.4513        | 0.56  | 350  | 0.4349          | 0.8133   |
  | 0.4647        | 0.64  | 400  | 0.4046          | 0.8333   |
  | 0.4827        | 0.72  | 450  | 0.4218          | 0.8333   |
  | 0.4517        | 0.8   | 500  | 0.4093          | 0.82     |
  | 0.4417        | 0.88  | 550  | 0.3999          | 0.84     |
  | 0.4701        | 0.96  | 600  | 0.3779          | 0.8867   |
  | 0.397         | 1.04  | 650  | 0.3730          | 0.8667   |
  | 0.3377        | 1.12  | 700  | 0.3833          | 0.8333   |
  | 0.411         | 1.2   | 750  | 0.3704          | 0.84     |
  | 0.3796        | 1.28  | 800  | 0.3472          | 0.86     |
  | 0.3523        | 1.36  | 850  | 0.3512          | 0.8733   |
  | 0.3992        | 1.44  | 900  | 0.3712          | 0.84     |
  | 0.3641        | 1.52  | 950  | 0.3718          | 0.82     |
  | 0.3973        | 1.6   | 1000 | 0.3508          | 0.84     |
  | 0.3576        | 1.68  | 1050 | 0.3600          | 0.86     |
  | 0.3701        | 1.76  | 1100 | 0.3287          | 0.8667   |
  | 0.3721        | 1.84  | 1150 | 0.3794          | 0.82     |
  | 0.3673        | 1.92  | 1200 | 0.3378          | 0.8733   |
  | 0.4223        | 2.0   | 1250 | 0.3508          | 0.86     |
  | 0.2745        | 2.08  | 1300 | 0.3835          | 0.86     |
  | 0.283         | 2.16  | 1350 | 0.3500          | 0.8533   |
  | 0.2769        | 2.24  | 1400 | 0.3334          | 0.8733   |
  | 0.2491        | 2.32  | 1450 | 0.3519          | 0.8867   |
  | 0.3237        | 2.4   | 1500 | 0.3438          | 0.86     |
  | 0.2662        | 2.48  | 1550 | 0.3513          | 0.8667   |
  | 0.2423        | 2.56  | 1600 | 0.3413          | 0.8867   |
  | 0.2655        | 2.64  | 1650 | 0.3126          | 0.8933   |
  | 0.2516        | 2.72  | 1700 | 0.3333          | 0.8733   |
  | 0.252         | 2.8   | 1750 | 0.3316          | 0.88     |
  | 0.2872        | 2.88  | 1800 | 0.3227          | 0.9      |
  | 0.306         | 2.96  | 1850 | 0.3383          | 0.8733   |
  | 0.248         | 3.04  | 1900 | 0.3474          | 0.8733   |
  | 0.1507        | 3.12  | 1950 | 0.4140          | 0.8667   |
  | 0.1994        | 3.2   | 2000 | 0.3729          | 0.8533   |
  | 0.167         | 3.28  | 2050 | 0.3782          | 0.8867   |
  | 0.1872        | 3.36  | 2100 | 0.4352          | 0.8867   |
  | 0.1611        | 3.44  | 2150 | 0.4511          | 0.8667   |
  | 0.2338        | 3.52  | 2200 | 0.4244          | 0.8533   |
  | 0.1538        | 3.6   | 2250 | 0.4226          | 0.8733   |
  | 0.1561        | 3.68  | 2300 | 0.4126          | 0.88     |
  | 0.2156        | 3.76  | 2350 | 0.4382          | 0.86     |
  | 0.1684        | 3.84  | 2400 | 0.4969          | 0.86     |
  | 0.1917        | 3.92  | 2450 | 0.4439          | 0.8667   |
  | 0.1584        | 4.0   | 2500 | 0.4759          | 0.86     |
  | 0.1038        | 4.08  | 2550 | 0.5042          | 0.8667   |
  | 0.0983        | 4.16  | 2600 | 0.5527          | 0.8533   |
  | 0.1404        | 4.24  | 2650 | 0.5801          | 0.84     |
  | 0.0844        | 4.32  | 2700 | 0.5884          | 0.86     |
  | 0.1347        | 4.4   | 2750 | 0.5865          | 0.8467   |
  | 0.1373        | 4.48  | 2800 | 0.5915          | 0.8533   |
  | 0.1506        | 4.56  | 2850 | 0.5976     