readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - nlu
  - domain-classificatoin
  metrics:
  - accuracy
  - f1
  model-index:
  - name: xlm-r-base-amazon-massive-domain
    results:
    - task:
        name: text-classification
        type: text-classification
      dataset:
        name: MASSIVE
        type: AmazonScience/massive
        split: test
      metrics:
      - name: F1
        type: f1
        value: 0.9213
  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-domain
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset).
  It achieves the following results on the evaluation set:
  - Loss: 0.3788
  - Accuracy: 0.9213
  - F1: 0.9213
  
  ## Model description
  
  Domain classifier trained from Amazon Massive dataset.
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 1.382         | 1.0   | 720  | 0.4533          | 0.8795   | 0.8795 |
  | 0.4598        | 2.0   | 1440 | 0.3448          | 0.9026   | 0.9026 |
  | 0.2547        | 3.0   | 2160 | 0.3762          | 0.9065   | 0.9065 |
  | 0.1986        | 4.0   | 2880 | 0.3748          | 0.9139   | 0.9139 |
  | 0.1358        | 5.0   | 3600 | 0.3788          | 0.9213   | 0.9213 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1