readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - x_glue
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-uncased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: x_glue
        type: x_glue
        args: ner
      metrics:
      - name: Precision
        type: precision
        value: 0.09187560910782316
      - name: Recall
        type: recall
        value: 0.1248795761078998
      - name: F1
        type: f1
        value: 0.10586493798172632
      - name: Accuracy
        type: accuracy
        value: 0.492660102891609
  ---
  
  <!-- 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. -->
  
  # bert-base-uncased-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the x_glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7979
  - Precision: 0.0919
  - Recall: 0.1249
  - F1: 0.1059
  - Accuracy: 0.4927
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1773        | 1.0   | 878  | 1.7953          | 0.1025    | 0.1352 | 0.1166 | 0.5058   |
  | 0.0397        | 2.0   | 1756 | 2.0827          | 0.0906    | 0.1230 | 0.1043 | 0.4888   |
  | 0.022         | 3.0   | 2634 | 2.8677          | 0.0864    | 0.1260 | 0.1025 | 0.4098   |
  | 0.0126        | 4.0   | 3512 | 2.8584          | 0.0848    | 0.1201 | 0.0994 | 0.4424   |
  | 0.0085        | 5.0   | 4390 | 2.7979          | 0.0919    | 0.1249 | 0.1059 | 0.4927   |
  
  
  ### Framework versions
  
  - Transformers 4.10.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  