readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - nerd
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: ner_nerd_fine
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nerd
        type: nerd
        args: nerd
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9050232835369201
  ---
  
  <!-- 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. -->
  
  # ner_nerd_fine
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3373
  - Precision: 0.6326
  - Recall: 0.6734
  - F1: 0.6524
  - Accuracy: 0.9050
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6219        | 1.0   | 8235  | 0.3347          | 0.6066    | 0.6581 | 0.6313 | 0.9015   |
  | 0.3071        | 2.0   | 16470 | 0.3165          | 0.6349    | 0.6637 | 0.6490 | 0.9060   |
  | 0.2384        | 3.0   | 24705 | 0.3311          | 0.6373    | 0.6769 | 0.6565 | 0.9068   |
  | 0.1834        | 4.0   | 32940 | 0.3414          | 0.6349    | 0.6780 | 0.6557 | 0.9069   |
  | 0.1392        | 5.0   | 41175 | 0.3793          | 0.6334    | 0.6775 | 0.6547 | 0.9068   |
  
  
  ### Framework versions
  
  - Transformers 4.9.1
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.2
  