readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: xlm-roberta-base-finetuned-emotion-37-labels
    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. -->
  
  # xlm-roberta-base-finetuned-emotion-37-labels
  
  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: 1.1765
  - Accuracy: 0.7185
  - F1: 0.7178
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 2.4256        | 1.0   | 433  | 1.7594          | 0.4384   | 0.4079 |
  | 1.5536        | 2.0   | 866  | 1.3105          | 0.5784   | 0.5631 |
  | 1.1753        | 3.0   | 1299 | 1.1767          | 0.6163   | 0.6057 |
  | 0.9378        | 4.0   | 1732 | 1.0613          | 0.6565   | 0.6542 |
  | 0.7606        | 5.0   | 2165 | 1.0284          | 0.6808   | 0.6776 |
  | 0.6167        | 6.0   | 2598 | 1.0128          | 0.6892   | 0.6888 |
  | 0.5009        | 7.0   | 3031 | 1.0250          | 0.6973   | 0.6946 |
  | 0.4083        | 8.0   | 3464 | 1.0506          | 0.7014   | 0.6996 |
  | 0.328         | 9.0   | 3897 | 1.0658          | 0.7075   | 0.7079 |
  | 0.2704        | 10.0  | 4330 | 1.0874          | 0.7106   | 0.7094 |
  | 0.2203        | 11.0  | 4763 | 1.1587          | 0.7031   | 0.7010 |
  | 0.1813        | 12.0  | 5196 | 1.1559          | 0.7141   | 0.7130 |
  | 0.1552        | 13.0  | 5629 | 1.1483          | 0.7173   | 0.7164 |
  | 0.1325        | 14.0  | 6062 | 1.1697          | 0.7173   | 0.7170 |
  | 0.1239        | 15.0  | 6495 | 1.1765          | 0.7185   | 0.7178 |
  
  
  ### Framework versions
  
  - Transformers 4.19.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.16.1
  - Tokenizers 0.12.1
  