readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  model-index:
  - name: Sentiment140_XLNET_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_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3797
  - 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: 3e-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: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6687        | 0.08  | 50   | 0.5194          | 0.76     |
  | 0.5754        | 0.16  | 100  | 0.4500          | 0.7867   |
  | 0.5338        | 0.24  | 150  | 0.3725          | 0.8333   |
  | 0.5065        | 0.32  | 200  | 0.4093          | 0.8133   |
  | 0.4552        | 0.4   | 250  | 0.3910          | 0.8267   |
  | 0.5352        | 0.48  | 300  | 0.3888          | 0.82     |
  | 0.415         | 0.56  | 350  | 0.3887          | 0.8267   |
  | 0.4716        | 0.64  | 400  | 0.3888          | 0.84     |
  | 0.4565        | 0.72  | 450  | 0.3619          | 0.84     |
  | 0.4447        | 0.8   | 500  | 0.3758          | 0.8333   |
  | 0.4407        | 0.88  | 550  | 0.3664          | 0.8133   |
  | 0.46          | 0.96  | 600  | 0.3797          | 0.84     |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.3.2
  - Tokenizers 0.13.1
  