readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2002
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: mBERT-base-cased-NER-CONLL
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2002
        type: conll2002
        args: es
      metrics:
      - name: Precision
        type: precision
        value: 0.8621083924079579
      - name: Recall
        type: recall
        value: 0.8662683823529411
      - name: F1
        type: f1
        value: 0.8641833810888252
      - name: Accuracy
        type: accuracy
        value: 0.9790639230580277
  ---
  
  <!-- 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. -->
  
  # mBERT-base-cased-NER-CONLL (EN-ES)
  
  This model is a fine-tuned version of [bert-base-multilingual-cased ](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 and conll2002 datasets. Training was performed separately.
  It achieves the following results on the evaluation set:
  
  Connll2003:
  - Loss: 0.0585
  - Precision: 0.9489
  - Recall: 0.9541
  - F1: 0.9515
  - Accuracy: 0.9880
  
  Conll2002:
  - Loss: 0.1435
  - Precision: 0.8621
  - Recall: 0.8663
  - F1: 0.8642
  - Accuracy: 0.9791
  
  ## Model description
  
  IOB tagging Scheme. PER/LOC/MISC/ORG tags
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Conll2002/2003 (ES-EN)
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  Conll2003:
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1739        | 1.0   | 878  | 0.0741          | 0.9246    | 0.9181 | 0.9213 | 0.9823   |
  | 0.045         | 2.0   | 1756 | 0.0586          | 0.9469    | 0.9476 | 0.9472 | 0.9870   |
  | 0.0213        | 3.0   | 2634 | 0.0583          | 0.9503    | 0.9510 | 0.9506 | 0.9877   |
  | 0.0113        | 4.0   | 3512 | 0.0585          | 0.9489    | 0.9541 | 0.9515 | 0.9880   |
  
  
  Conll2002:
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0739        | 1.0   | 4162  | 0.1322          | 0.8430    | 0.8267 | 0.8348 | 0.9741   |
  | 0.0454        | 2.0   | 8324  | 0.1158          | 0.8664    | 0.8614 | 0.8639 | 0.9782   |
  | 0.031         | 3.0   | 12486 | 0.1243          | 0.8521    | 0.8660 | 0.8590 | 0.9783   |
  | 0.0136        | 4.0   | 16648 | 0.1435          | 0.8621    | 0.8663 | 0.8642 | 0.9791   |
  
  
  ### Framework versions
  
  - Transformers 4.12.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  