readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: custom-ner-model2
    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. -->
  
  # custom-ner-model2
  
  This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2050
  - Precision: 0.8542
  - Recall: 0.8817
  - F1: 0.8677
  - Accuracy: 0.9595
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 105  | 0.5185          | 0.5840    | 0.5484 | 0.5656 | 0.8596   |
  | No log        | 2.0   | 210  | 0.3212          | 0.7365    | 0.7312 | 0.7338 | 0.9050   |
  | No log        | 3.0   | 315  | 0.2440          | 0.8123    | 0.8065 | 0.8094 | 0.9389   |
  | No log        | 4.0   | 420  | 0.2186          | 0.8014    | 0.8100 | 0.8057 | 0.9431   |
  | 0.4107        | 5.0   | 525  | 0.1911          | 0.8481    | 0.8602 | 0.8541 | 0.9516   |
  | 0.4107        | 6.0   | 630  | 0.1931          | 0.8235    | 0.8530 | 0.8380 | 0.9546   |
  | 0.4107        | 7.0   | 735  | 0.1720          | 0.8368    | 0.8638 | 0.8501 | 0.9570   |
  | 0.4107        | 8.0   | 840  | 0.1858          | 0.8385    | 0.8746 | 0.8561 | 0.9583   |
  | 0.4107        | 9.0   | 945  | 0.1858          | 0.85      | 0.8530 | 0.8515 | 0.9552   |
  | 0.0667        | 10.0  | 1050 | 0.1961          | 0.8526    | 0.8710 | 0.8617 | 0.9564   |
  | 0.0667        | 11.0  | 1155 | 0.1970          | 0.8537    | 0.8781 | 0.8657 | 0.9589   |
  | 0.0667        | 12.0  | 1260 | 0.1865          | 0.8478    | 0.8781 | 0.8627 | 0.9619   |
  | 0.0667        | 13.0  | 1365 | 0.1994          | 0.8379    | 0.8710 | 0.8541 | 0.9583   |
  | 0.0667        | 14.0  | 1470 | 0.1913          | 0.8507    | 0.8781 | 0.8642 | 0.9613   |
  | 0.0274        | 15.0  | 1575 | 0.2064          | 0.8512    | 0.8817 | 0.8662 | 0.9595   |
  | 0.0274        | 16.0  | 1680 | 0.2053          | 0.8478    | 0.8781 | 0.8627 | 0.9601   |
  | 0.0274        | 17.0  | 1785 | 0.2037          | 0.8576    | 0.8853 | 0.8713 | 0.9601   |
  | 0.0274        | 18.0  | 1890 | 0.2056          | 0.8632    | 0.8817 | 0.8723 | 0.9595   |
  | 0.0274        | 19.0  | 1995 | 0.2066          | 0.8571    | 0.8817 | 0.8693 | 0.9601   |
  | 0.0162        | 20.0  | 2100 | 0.2050          | 0.8542    | 0.8817 | 0.8677 | 0.9595   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  