readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - amazon_reviews_multi
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: multi-minilm-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: 0.5422
      - name: F1
        type: f1
        value: 0.543454465221178
      - name: Precision
        type: precision
        value: 0.5452336215624385
      - name: Recall
        type: recall
        value: 0.5422
  ---
  
  <!-- 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. -->
  
  # multi-minilm-finetuned-amazon-review
  
  This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the amazon_reviews_multi dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2436
  - Accuracy: 0.5422
  - F1: 0.5435
  - Precision: 0.5452
  - Recall: 0.5422
  
  ## 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: 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 1.0049        | 1.0   | 2500  | 1.0616          | 0.5352   | 0.5268 | 0.5347    | 0.5352 |
  | 0.9172        | 2.0   | 5000  | 1.0763          | 0.5432   | 0.5412 | 0.5444    | 0.5432 |
  | 0.8285        | 3.0   | 7500  | 1.1077          | 0.5408   | 0.5428 | 0.5494    | 0.5408 |
  | 0.7361        | 4.0   | 10000 | 1.1743          | 0.5342   | 0.5399 | 0.5531    | 0.5342 |
  | 0.6538        | 5.0   | 12500 | 1.2436          | 0.5422   | 0.5435 | 0.5452    | 0.5422 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  