readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - nlu
  - slot-tagging
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-r-base-amazon-massive-slot
    results:
    - task:
        name: slot-filling
        type: slot-filling
      dataset:
        name: MASSIVE
        type: AmazonScience/massive
        split: test
      metrics:
      - name: F1
        type: f1
        value: 0.8405
  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-slot
  
  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: 0.5006
  - Precision: 0.8144
  - Recall: 0.8683
  - F1: 0.8405
  - Accuracy: 0.9333
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.1445        | 1.0   | 720   | 0.5446          | 0.6681    | 0.6770 | 0.6725 | 0.8842   |
  | 0.5908        | 2.0   | 1440  | 0.3869          | 0.7331    | 0.7706 | 0.7514 | 0.9083   |
  | 0.3228        | 3.0   | 2160  | 0.3285          | 0.7658    | 0.8288 | 0.7961 | 0.9219   |
  | 0.2561        | 4.0   | 2880  | 0.3063          | 0.7819    | 0.8402 | 0.8100 | 0.9257   |
  | 0.1808        | 5.0   | 3600  | 0.3000          | 0.8011    | 0.8429 | 0.8214 | 0.9305   |
  | 0.1487        | 6.0   | 4320  | 0.2982          | 0.8201    | 0.8492 | 0.8344 | 0.9361   |
  | 0.1156        | 7.0   | 5040  | 0.3252          | 0.8009    | 0.8569 | 0.8280 | 0.9313   |
  | 0.094         | 8.0   | 5760  | 0.3481          | 0.8127    | 0.8502 | 0.8310 | 0.9333   |
  | 0.0843        | 9.0   | 6480  | 0.3764          | 0.7990    | 0.8613 | 0.8290 | 0.9304   |
  | 0.0641        | 10.0  | 7200  | 0.3822          | 0.7930    | 0.8609 | 0.8256 | 0.9280   |
  | 0.0547        | 11.0  | 7920  | 0.3889          | 0.8223    | 0.8649 | 0.8431 | 0.9354   |
  | 0.04          | 12.0  | 8640  | 0.4416          | 0.8019    | 0.8633 | 0.8314 | 0.9288   |
  | 0.0368        | 13.0  | 9360  | 0.4339          | 0.8117    | 0.8606 | 0.8354 | 0.9328   |
  | 0.0297        | 14.0  | 10080 | 0.4698          | 0.8062    | 0.8623 | 0.8333 | 0.9314   |
  | 0.0227        | 15.0  | 10800 | 0.4763          | 0.8058    | 0.8656 | 0.8346 | 0.9327   |
  | 0.0185        | 16.0  | 11520 | 0.4793          | 0.8124    | 0.8613 | 0.8361 | 0.9326   |
  | 0.0182        | 17.0  | 12240 | 0.4835          | 0.8191    | 0.8629 | 0.8404 | 0.9341   |
  | 0.0147        | 18.0  | 12960 | 0.4981          | 0.8140    | 0.8693 | 0.8407 | 0.9336   |
  | 0.0111        | 19.0  | 13680 | 0.5002          | 0.8099    | 0.8719 | 0.8398 | 0.9340   |
  | 0.0128        | 20.0  | 14400 | 0.5006          | 0.8144    | 0.8683 | 0.8405 | 0.9333   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1