readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - nlu
  - intent-classification
  - text-classification
  metrics:
  - accuracy
  - f1
  model-index:
  - name: xlm-r-base-amazon-massive-intent-label_smoothing
    results:
    - task:
        name: intent-classification
        type: intent-classification
      dataset:
        name: MASSIVE
        type: AmazonScience/massive
        split: test
      metrics:
      - name: F1
        type: f1
        value: 0.8879
  datasets:
  - AmazonScience/massive
  language:
  - en
  ---
  
  <!-- 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. -->
  
  # xlm-r-base-amazon-massive-intent-label_smoothing
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.5148
  - Accuracy: 0.8879
  - F1: 0.8879
  
  ## 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
  - label_smoothing_factor: 0.4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 3.3945        | 1.0   | 720  | 2.7175          | 0.7900   | 0.7900 |
  | 2.7629        | 2.0   | 1440 | 2.5660          | 0.8549   | 0.8549 |
  | 2.5143        | 3.0   | 2160 | 2.5389          | 0.8711   | 0.8711 |
  | 2.4678        | 4.0   | 2880 | 2.5172          | 0.8883   | 0.8883 |
  | 2.4187        | 5.0   | 3600 | 2.5148          | 0.8879   | 0.8879 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2