readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xglue
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-tiny-finetuned-xglue-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: xglue
        type: xglue
        config: ner
        split: train
        args: ner
      metrics:
      - name: Precision
        type: precision
        value: 0.630759453447728
      - name: Recall
        type: recall
        value: 0.6681252103668799
      - name: F1
        type: f1
        value: 0.6489048708728343
      - name: Accuracy
        type: accuracy
        value: 0.9274310133922189
  ---
  
  <!-- 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-tiny-finetuned-xglue-ner
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2489
  - Precision: 0.6308
  - Recall: 0.6681
  - F1: 0.6489
  - Accuracy: 0.9274
  
  ## 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: 8
  - eval_batch_size: 8
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.4082        | 1.0   | 1756 | 0.3326          | 0.5600    | 0.5798 | 0.5697 | 0.9118   |
  | 0.2974        | 2.0   | 3512 | 0.2635          | 0.6143    | 0.6562 | 0.6346 | 0.9248   |
  | 0.2741        | 3.0   | 5268 | 0.2489          | 0.6308    | 0.6681 | 0.6489 | 0.9274   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  