readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  model-index:
  - name: roberta-base-finetuned-ner-kmeans
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.955868544600939
      - name: Recall
        type: recall
        value: 0.9614658103513412
      - name: F1
        type: f1
        value: 0.9586590074394953
  ---
  
  <!-- 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. -->
  
  # roberta-base-finetuned-ner-kmeans
  
  This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0592
  - Precision: 0.9559
  - Recall: 0.9615
  - F1: 0.9587
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
  | 0.0248        | 1.0   | 878  | 0.0609          | 0.9507    | 0.9561 | 0.9534 |
  | 0.0163        | 2.0   | 1756 | 0.0640          | 0.9515    | 0.9578 | 0.9546 |
  | 0.0089        | 3.0   | 2634 | 0.0592          | 0.9559    | 0.9615 | 0.9587 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  