readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: hasoc19-xlm-roberta-base-targinsult1
    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-targinsult1
  
  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: 0.7512
  - Accuracy: 0.7096
  - Precision: 0.6720
  - Recall: 0.6675
  - F1: 0.6695
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 263  | 0.5619          | 0.6996   | 0.6660    | 0.6717 | 0.6684 |
  | 0.5931        | 2.0   | 526  | 0.5350          | 0.7239   | 0.6880    | 0.6576 | 0.6655 |
  | 0.5931        | 3.0   | 789  | 0.5438          | 0.7239   | 0.6872    | 0.6644 | 0.6714 |
  | 0.5101        | 4.0   | 1052 | 0.5595          | 0.7196   | 0.6866    | 0.6909 | 0.6886 |
  | 0.5101        | 5.0   | 1315 | 0.5580          | 0.7186   | 0.6818    | 0.6743 | 0.6774 |
  | 0.4313        | 6.0   | 1578 | 0.6000          | 0.7039   | 0.6679    | 0.6692 | 0.6686 |
  | 0.4313        | 7.0   | 1841 | 0.6429          | 0.7082   | 0.6765    | 0.6841 | 0.6794 |
  | 0.3591        | 8.0   | 2104 | 0.6626          | 0.7115   | 0.6772    | 0.6803 | 0.6786 |
  | 0.3591        | 9.0   | 2367 | 0.7231          | 0.7139   | 0.6764    | 0.6700 | 0.6727 |
  | 0.3016        | 10.0  | 2630 | 0.7512          | 0.7096   | 0.6720    | 0.6675 | 0.6695 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  