readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-es-base-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-es-base-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: 0.2887
  - Precision: 0.5703
  - Recall: 0.6028
  - F1: 0.5861
  - Accuracy: 0.9216
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7989        | 1.0   | 515  | 0.4610          | 0.4365    | 0.3851 | 0.4091 | 0.8867   |
  | 0.4088        | 2.0   | 1030 | 0.3468          | 0.5133    | 0.5175 | 0.5154 | 0.9067   |
  | 0.3144        | 3.0   | 1545 | 0.3082          | 0.5492    | 0.5532 | 0.5512 | 0.9158   |
  | 0.2675        | 4.0   | 2060 | 0.2913          | 0.5627    | 0.5865 | 0.5744 | 0.9216   |
  | 0.239         | 5.0   | 2575 | 0.2887          | 0.5703    | 0.6028 | 0.5861 | 0.9216   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  