readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hasoc19-xlm-roberta-base-sentiment-new
    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. -->
  
  # hasoc19-xlm-roberta-base-sentiment-new
  
  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: 0.3840
  - Accuracy: 0.8726
  - Precision: 0.8724
  - Recall: 0.8726
  - F1: 0.8725
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.4786        | 1.0   | 537  | 0.3999          | 0.8381   | 0.8391    | 0.8381 | 0.8363 |
  | 0.349         | 2.0   | 1074 | 0.3443          | 0.8606   | 0.8603    | 0.8606 | 0.8603 |
  | 0.2927        | 3.0   | 1611 | 0.3412          | 0.8669   | 0.8668    | 0.8669 | 0.8662 |
  | 0.2471        | 4.0   | 2148 | 0.3408          | 0.8705   | 0.8708    | 0.8705 | 0.8706 |
  | 0.2195        | 5.0   | 2685 | 0.3897          | 0.8726   | 0.8725    | 0.8726 | 0.8721 |
  | 0.1849        | 6.0   | 3222 | 0.3840          | 0.8726   | 0.8724    | 0.8726 | 0.8725 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  