readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - nerd
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: ner_nerd
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nerd
        type: nerd
        args: nerd
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9391592461061087
  ---
  
  <!-- 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
  
  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: 0.2245
  - Precision: 0.7466
  - Recall: 0.7873
  - F1: 0.7664
  - Accuracy: 0.9392
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2843        | 1.0   | 8235  | 0.1951          | 0.7352    | 0.7824 | 0.7580 | 0.9375   |
  | 0.1655        | 2.0   | 16470 | 0.1928          | 0.7519    | 0.7827 | 0.7670 | 0.9398   |
  | 0.1216        | 3.0   | 24705 | 0.2119          | 0.75      | 0.7876 | 0.7684 | 0.9396   |
  | 0.0881        | 4.0   | 32940 | 0.2258          | 0.7515    | 0.7896 | 0.7701 | 0.9392   |
  | 0.0652        | 5.0   | 41175 | 0.2564          | 0.7518    | 0.7875 | 0.7692 | 0.9387   |
  
  
  ### Framework versions
  
  - Transformers 4.9.1
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.2
  