readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: edos-2023-baseline-xlm-roberta-base-label_vector
    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. -->
  
  # edos-2023-baseline-xlm-roberta-base-label_vector
  
  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: 1.5797
  - F1: 0.2746
  
  ## 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
  - lr_scheduler_warmup_steps: 5
  - num_epochs: 12
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.1596        | 1.18  | 100  | 1.9772          | 0.0891 |
  | 1.8651        | 2.35  | 200  | 1.7720          | 0.1159 |
  | 1.6848        | 3.53  | 300  | 1.7193          | 0.1892 |
  | 1.5532        | 4.71  | 400  | 1.6794          | 0.2191 |
  | 1.466         | 5.88  | 500  | 1.6095          | 0.2419 |
  | 1.3562        | 7.06  | 600  | 1.5771          | 0.2694 |
  | 1.2909        | 8.24  | 700  | 1.5761          | 0.2707 |
  | 1.2027        | 9.41  | 800  | 1.5747          | 0.2764 |
  | 1.192         | 10.59 | 900  | 1.5893          | 0.2686 |
  | 1.1256        | 11.76 | 1000 | 1.5797          | 0.2746 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  