readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-it-aug-ner
    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-it-aug-ner
  
  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: 0.2626
  - Precision: 0.6778
  - Recall: 0.6981
  - F1: 0.6878
  - Accuracy: 0.9266
  
  ## 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: 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 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6114        | 1.0   | 882  | 0.3615          | 0.5708    | 0.5680 | 0.5694 | 0.9023   |
  | 0.2401        | 2.0   | 1764 | 0.2841          | 0.6274    | 0.6674 | 0.6468 | 0.9170   |
  | 0.1886        | 3.0   | 2646 | 0.2720          | 0.6580    | 0.6853 | 0.6713 | 0.9224   |
  | 0.1594        | 4.0   | 3528 | 0.2626          | 0.6778    | 0.6981 | 0.6878 | 0.9266   |
  | 0.1454        | 5.0   | 4410 | 0.2632          | 0.6660    | 0.6974 | 0.6813 | 0.9257   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  