readme: ---
  license: apache-2.0
  tags:
  - classification
  - generated_from_trainer
  datasets:
  - poem_sentiment
  metrics:
  - accuracy
  model-index:
  - name: clasificador-poem-sentiment
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: poem_sentiment
        type: poem_sentiment
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9038461538461539
  ---
  
  <!-- 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. -->
  
  # clasificador-poem-sentiment
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the poem_sentiment dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5088
  - Accuracy: 0.9038
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 112  | 0.4324          | 0.8654   |
  | No log        | 2.0   | 224  | 0.4070          | 0.875    |
  | No log        | 3.0   | 336  | 0.5088          | 0.9038   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  