readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-finetuned-ner-false-finetuned-ner-2002
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.941186271242919
      - name: Recall
        type: recall
        value: 0.9506900033658701
      - name: F1
        type: f1
        value: 0.945914266577361
      - name: Accuracy
        type: accuracy
        value: 0.9904209337642615
  ---
  
  <!-- 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-finetuned-ner-false-finetuned-ner-2002
  
  This model is a fine-tuned version of [StivenLancheros/xlm-roberta-base-finetuned-ner-false](https://huggingface.co/StivenLancheros/xlm-roberta-base-finetuned-ner-false) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0725
  - Precision: 0.9412
  - Recall: 0.9507
  - F1: 0.9459
  - Accuracy: 0.9904
  
  ## 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: 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
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.086         | 1.0   | 7021  | 0.0709          | 0.9221    | 0.9261 | 0.9241 | 0.9872   |
  | 0.0352        | 2.0   | 14042 | 0.0871          | 0.9243    | 0.9354 | 0.9298 | 0.9879   |
  | 0.0203        | 3.0   | 21063 | 0.0747          | 0.9398    | 0.9490 | 0.9444 | 0.9901   |
  | 0.0184        | 4.0   | 28084 | 0.0725          | 0.9412    | 0.9507 | 0.9459 | 0.9904   |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  