readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: roberta-fine-sentiment-hineng-concat
    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. -->
  
  # roberta-fine-sentiment-hineng-concat
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1126
  - Accuracy: 0.8669
  - Precision: 0.8667
  - Recall: 0.8669
  - F1: 0.8668
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.5814        | 1.0   | 4293  | 0.6920          | 0.8249   | 0.8304    | 0.8249 | 0.8257 |
  | 0.5169        | 2.0   | 8586  | 0.5919          | 0.8459   | 0.8499    | 0.8459 | 0.8465 |
  | 0.4274        | 3.0   | 12879 | 0.7775          | 0.8512   | 0.8513    | 0.8512 | 0.8504 |
  | 0.3246        | 4.0   | 17172 | 0.7757          | 0.8522   | 0.8593    | 0.8522 | 0.8528 |
  | 0.22          | 5.0   | 21465 | 0.9306          | 0.8574   | 0.8574    | 0.8574 | 0.8574 |
  | 0.1226        | 6.0   | 25758 | 0.9663          | 0.8627   | 0.8632    | 0.8627 | 0.8628 |
  | 0.085         | 7.0   | 30051 | 1.0266          | 0.8653   | 0.8651    | 0.8653 | 0.8651 |
  | 0.0713        | 8.0   | 34344 | 1.1126          | 0.8669   | 0.8667    | 0.8669 | 0.8668 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  