readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - amazon_reviews_multi
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: distilbert-base-uncased-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: 0.693
      - name: F1
        type: f1
        value: 0.7002653469272611
      - name: Precision
        type: precision
        value: 0.709541681233075
      - name: Recall
        type: recall
        value: 0.693
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-amazon-review
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3494
  - Accuracy: 0.693
  - F1: 0.7003
  - Precision: 0.7095
  - Recall: 0.693
  
  ## 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  | 0.8287          | 0.7104   | 0.7120 | 0.7152    | 0.7104 |
  | 0.4238        | 1.0   | 1000 | 0.8917          | 0.7094   | 0.6989 | 0.6917    | 0.7094 |
  | 0.4238        | 1.5   | 1500 | 0.9367          | 0.6884   | 0.6983 | 0.7151    | 0.6884 |
  | 0.3152        | 2.0   | 2000 | 0.9845          | 0.7116   | 0.7144 | 0.7176    | 0.7116 |
  | 0.3152        | 2.5   | 2500 | 1.0752          | 0.6814   | 0.6968 | 0.7232    | 0.6814 |
  | 0.2454        | 3.0   | 3000 | 1.1215          | 0.6918   | 0.6954 | 0.7068    | 0.6918 |
  | 0.2454        | 3.5   | 3500 | 1.2905          | 0.6976   | 0.7048 | 0.7138    | 0.6976 |
  | 0.1989        | 4.0   | 4000 | 1.2938          | 0.694    | 0.7016 | 0.7113    | 0.694  |
  | 0.1989        | 4.5   | 4500 | 1.3623          | 0.6972   | 0.7014 | 0.7062    | 0.6972 |
  | 0.1746        | 5.0   | 5000 | 1.3494          | 0.693    | 0.7003 | 0.7095    | 0.693  |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  