readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - nlu
  - intent-classification
  metrics:
  - accuracy
  - f1
  model-index:
  - name: multilingual_minilm-amazon-massive-intent
    results:
    - task: 
        name: intent-classification
        type: intent-classification  
      dataset:
        name: MASSIVE
        type: AmazonScience/massive
        split: test
      metrics:
        - name: F1
          type: f1
          value: 0.8234
  datasets:
  - AmazonScience/massive
  language:
  - en
  pipeline_tag: text-classification
  ---
  
  <!-- 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. -->
  
  # multilingual_minilm-amazon-massive-intent
  
  This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8941
  - Accuracy: 0.8234
  - F1: 0.8234
  
  ## 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: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
  | 3.7961        | 1.0   | 720   | 3.1657          | 0.3404   | 0.3404 |
  | 3.1859        | 2.0   | 1440  | 2.4835          | 0.4343   | 0.4343 |
  | 2.3104        | 3.0   | 2160  | 2.0474          | 0.5652   | 0.5652 |
  | 2.0071        | 4.0   | 2880  | 1.7190          | 0.6503   | 0.6503 |
  | 1.5595        | 5.0   | 3600  | 1.4873          | 0.6990   | 0.6990 |
  | 1.3664        | 6.0   | 4320  | 1.3088          | 0.7354   | 0.7354 |
  | 1.1272        | 7.0   | 5040  | 1.1964          | 0.7521   | 0.7521 |
  | 1.0128        | 8.0   | 5760  | 1.1115          | 0.7718   | 0.7718 |
  | 0.9405        | 9.0   | 6480  | 1.0598          | 0.7841   | 0.7841 |
  | 0.7758        | 10.0  | 7200  | 1.0003          | 0.7944   | 0.7944 |
  | 0.7457        | 11.0  | 7920  | 0.9599          | 0.8037   | 0.8037 |
  | 0.6605        | 12.0  | 8640  | 0.9175          | 0.8165   | 0.8165 |
  | 0.6135        | 13.0  | 9360  | 0.9148          | 0.8190   | 0.8190 |
  | 0.5698        | 14.0  | 10080 | 0.8976          | 0.8229   | 0.8229 |
  | 0.5578        | 15.0  | 10800 | 0.8941          | 0.8234   | 0.8234 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2