readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - amazon_reviews_multi
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: selectra-small-finetuned-amazon-review
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: amazon_reviews_multi
        type: amazon_reviews_multi
        args: es
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.737
      - name: F1
        type: f1
        value: 0.7437773019932409
      - name: Precision
        type: precision
        value: 0.7524857881639091
      - name: Recall
        type: recall
        value: 0.737
  ---
  
  <!-- 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. -->
  
  # selectra-small-finetuned-amazon-review
  
  This model is a fine-tuned version of [Recognai/selectra_small](https://huggingface.co/Recognai/selectra_small) on the amazon_reviews_multi dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6279
  - Accuracy: 0.737
  - F1: 0.7438
  - Precision: 0.7525
  - Recall: 0.737
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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 | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 0.5   | 500  | 0.7041          | 0.7178   | 0.6724 | 0.6715    | 0.7178 |
  | 0.7908        | 1.0   | 1000 | 0.6365          | 0.7356   | 0.7272 | 0.7211    | 0.7356 |
  | 0.7908        | 1.5   | 1500 | 0.6204          | 0.7376   | 0.7380 | 0.7387    | 0.7376 |
  | 0.6358        | 2.0   | 2000 | 0.6162          | 0.7386   | 0.7377 | 0.7380    | 0.7386 |
  | 0.6358        | 2.5   | 2500 | 0.6228          | 0.7274   | 0.7390 | 0.7576    | 0.7274 |
  | 0.5827        | 3.0   | 3000 | 0.6188          | 0.7378   | 0.7400 | 0.7425    | 0.7378 |
  | 0.5827        | 3.5   | 3500 | 0.6246          | 0.7374   | 0.7416 | 0.7467    | 0.7374 |
  | 0.5427        | 4.0   | 4000 | 0.6266          | 0.7446   | 0.7452 | 0.7465    | 0.7446 |
  | 0.5427        | 4.5   | 4500 | 0.6331          | 0.7392   | 0.7421 | 0.7456    | 0.7392 |
  | 0.5184        | 5.0   | 5000 | 0.6279          | 0.737    | 0.7438 | 0.7525    | 0.737  |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  