readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: custom-ner-model
    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-model
  
  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: 0.2706
  - Precision: 0.8139
  - Recall: 0.8624
  - F1: 0.8374
  - Accuracy: 0.9376
  
  ## 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   | 55   | 0.2457          | 0.7716    | 0.8211 | 0.7956 | 0.9346   |
  | No log        | 2.0   | 110  | 0.3008          | 0.75      | 0.8257 | 0.7860 | 0.9152   |
  | No log        | 3.0   | 165  | 0.2658          | 0.7712    | 0.8349 | 0.8018 | 0.9322   |
  | No log        | 4.0   | 220  | 0.2580          | 0.7583    | 0.8349 | 0.7948 | 0.9322   |
  | No log        | 5.0   | 275  | 0.2610          | 0.8079    | 0.8486 | 0.8277 | 0.9346   |
  | No log        | 6.0   | 330  | 0.2660          | 0.7699    | 0.8440 | 0.8053 | 0.9303   |
  | No log        | 7.0   | 385  | 0.2910          | 0.7830    | 0.8440 | 0.8124 | 0.9261   |
  | No log        | 8.0   | 440  | 0.2577          | 0.7913    | 0.8349 | 0.8125 | 0.9376   |
  | No log        | 9.0   | 495  | 0.2629          | 0.7948    | 0.8349 | 0.8143 | 0.9346   |
  | 0.0437        | 10.0  | 550  | 0.2745          | 0.8070    | 0.8440 | 0.8251 | 0.9328   |
  | 0.0437        | 11.0  | 605  | 0.2733          | 0.7860    | 0.8257 | 0.8054 | 0.9316   |
  | 0.0437        | 12.0  | 660  | 0.2641          | 0.7965    | 0.8440 | 0.8196 | 0.9310   |
  | 0.0437        | 13.0  | 715  | 0.2716          | 0.8139    | 0.8624 | 0.8374 | 0.9334   |
  | 0.0437        | 14.0  | 770  | 0.2765          | 0.8210    | 0.8624 | 0.8412 | 0.9334   |
  | 0.0437        | 15.0  | 825  | 0.2775          | 0.8253    | 0.8670 | 0.8456 | 0.9346   |
  | 0.0437        | 16.0  | 880  | 0.2737          | 0.7897    | 0.8440 | 0.8160 | 0.9340   |
  | 0.0437        | 17.0  | 935  | 0.2691          | 0.8043    | 0.8486 | 0.8259 | 0.9388   |
  | 0.0437        | 18.0  | 990  | 0.2694          | 0.8246    | 0.8624 | 0.8430 | 0.9382   |
  | 0.0157        | 19.0  | 1045 | 0.2732          | 0.8069    | 0.8624 | 0.8337 | 0.9346   |
  | 0.0157        | 20.0  | 1100 | 0.2706          | 0.8139    | 0.8624 | 0.8374 | 0.9376   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  