readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-small-finetuned-xglue-ner-longer20
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        config: wnut_17
        split: train
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.5782747603833865
      - name: Recall
        type: recall
        value: 0.43301435406698563
      - name: F1
        type: f1
        value: 0.4952120383036936
      - name: Accuracy
        type: accuracy
        value: 0.92613831861452
  ---
  
  <!-- 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-small-finetuned-xglue-ner-longer20
  
  This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer10](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner-longer10) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5839
  - Precision: 0.5783
  - Recall: 0.4330
  - F1: 0.4952
  - Accuracy: 0.9261
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 425  | 0.4882          | 0.5       | 0.4510 | 0.4742 | 0.9228   |
  | 0.013         | 2.0   | 850  | 0.5039          | 0.5130    | 0.4246 | 0.4647 | 0.9242   |
  | 0.0124        | 3.0   | 1275 | 0.5331          | 0.5506    | 0.4426 | 0.4907 | 0.9256   |
  | 0.0159        | 4.0   | 1700 | 0.5403          | 0.5488    | 0.4234 | 0.4781 | 0.9249   |
  | 0.0132        | 5.0   | 2125 | 0.5459          | 0.5714    | 0.4211 | 0.4848 | 0.9255   |
  | 0.0117        | 6.0   | 2550 | 0.5522          | 0.5637    | 0.4342 | 0.4905 | 0.9253   |
  | 0.0117        | 7.0   | 2975 | 0.5712          | 0.5778    | 0.4354 | 0.4966 | 0.9257   |
  | 0.007         | 8.0   | 3400 | 0.5860          | 0.5828    | 0.4378 | 0.5    | 0.9259   |
  | 0.0066        | 9.0   | 3825 | 0.5745          | 0.5703    | 0.4462 | 0.5007 | 0.9259   |
  | 0.0049        | 10.0  | 4250 | 0.5839          | 0.5783    | 0.4330 | 0.4952 | 0.9261   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  