readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - x_glue
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-NER-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: 0.2273838630806846
      - name: Recall
        type: recall
        value: 0.11185727172496743
      - name: F1
        type: f1
        value: 0.14994961370507223
      - name: Accuracy
        type: accuracy
        value: 0.8485324947589099
  ---
  
  <!-- 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-NER-finetuned-ner
  
  This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the x_glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4380
  - Precision: 0.2274
  - Recall: 0.1119
  - F1: 0.1499
  - Accuracy: 0.8485
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0822        | 1.0   | 878  | 1.1648          | 0.2068    | 0.1101 | 0.1437 | 0.8471   |
  | 0.0102        | 2.0   | 1756 | 1.2697          | 0.2073    | 0.1110 | 0.1445 | 0.8447   |
  | 0.0049        | 3.0   | 2634 | 1.3945          | 0.2006    | 0.1073 | 0.1399 | 0.8368   |
  | 0.0025        | 4.0   | 3512 | 1.3994          | 0.2243    | 0.1126 | 0.1499 | 0.8501   |
  | 0.0011        | 5.0   | 4390 | 1.4380          | 0.2274    | 0.1119 | 0.1499 | 0.8485   |
  
  
  ### Framework versions
  
  - Transformers 4.10.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  