readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: mdeberta-targin-final
    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. -->
  
  # mdeberta-targin-final
  
  This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5637
  - Accuracy: 0.7091
  - Precision: 0.6841
  - Recall: 0.6557
  - F1: 0.6617
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 296  | 0.6001          | 0.6435   | 0.6344    | 0.5087 | 0.4156 |
  | 0.6011        | 2.0   | 592  | 0.5633          | 0.7091   | 0.6879    | 0.6464 | 0.6521 |
  | 0.6011        | 3.0   | 888  | 0.5501          | 0.7234   | 0.6991    | 0.6841 | 0.6892 |
  | 0.5401        | 4.0   | 1184 | 0.5558          | 0.7082   | 0.6818    | 0.6595 | 0.6652 |
  | 0.5401        | 5.0   | 1480 | 0.5637          | 0.7091   | 0.6841    | 0.6557 | 0.6617 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  