readme: ---
  metrics:
    - precision: 0.936
    - recall: 0.9458
    - f1: 0.9409
    - accuracy: 0.9902
  datasets:
    - conll2003  
  language: 
  - en 
  - de 
  - nl 
  - es
  - multilingual
  
  model-index:
  - name: gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
    results:
    - task: 
        type: ner
        name: Named Entity Recognition
      dataset:
        type: conll2003
        name: ConLL 2003
      metrics:
        - type: f1-score
          value: 0.9409
  ---
  
  <!-- 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. -->
  
  # gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
  
  This model was trained from scratch on an conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0388
  - Precision: 0.9360
  - Recall: 0.9458
  - F1: 0.9409
  - Accuracy: 0.9902
  
  ## Model description
  
  It is based on distilbert-base-multilingual-cased
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Training dataset: [conll2003](https://huggingface.co/datasets/conll2003)
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1653        | 1.0   | 878  | 0.0465          | 0.9267    | 0.9300 | 0.9283 | 0.9883   |
  | 0.0322        | 2.0   | 1756 | 0.0404          | 0.9360    | 0.9431 | 0.9396 | 0.9897   |
  | 0.0185        | 3.0   | 2634 | 0.0388          | 0.9360    | 0.9458 | 0.9409 | 0.9902   |
  
  
  ### Framework versions
  
  - Transformers 4.6.1
  - Pytorch 1.8.1+cu101
  - Datasets 1.6.2
  - Tokenizers 0.10.2
  