readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: tluo_xml_roberta_base_amazon_review_sentiment_v3
    results: []
  ---
  
  <!-- 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. -->
  
  # tluo_xml_roberta_base_amazon_review_sentiment_v3
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9456
  - Accuracy: 0.6023
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 123
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.056         | 0.33  | 5000  | 0.9885          | 0.5642   |
  | 0.944         | 0.67  | 10000 | 0.9574          | 0.5913   |
  | 0.9505        | 1.0   | 15000 | 0.9674          | 0.579    |
  | 0.8902        | 1.33  | 20000 | 0.9660          | 0.5945   |
  | 0.8851        | 1.67  | 25000 | 0.9470          | 0.5888   |
  | 0.8714        | 2.0   | 30000 | 0.9456          | 0.6023   |
  | 0.7967        | 2.33  | 35000 | 0.9662          | 0.5978   |
  | 0.767         | 2.67  | 40000 | 0.9738          | 0.5987   |
  | 0.7595        | 3.0   | 45000 | 0.9740          | 0.5988   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Tokenizers 0.13.1
  