readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: distilbert-base-uncased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9844313470062116
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-ner
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0590
  - Precision: 0.9266
  - Recall: 0.9381
  - F1: 0.9323
  - Accuracy: 0.9844
  
  ## 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     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0616        | 1.0   | 878  | 0.0604          | 0.9195    | 0.9370 | 0.9282 | 0.9833   |
  | 0.0328        | 2.0   | 1756 | 0.0588          | 0.9258    | 0.9375 | 0.9316 | 0.9841   |
  | 0.0246        | 3.0   | 2634 | 0.0590          | 0.9266    | 0.9381 | 0.9323 | 0.9844   |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.8.1
  - Datasets 1.9.0
  - Tokenizers 0.10.3
  