readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-tiny-finetuned-wnut17-ner
    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: 0.0
      - name: Recall
        type: recall
        value: 0.0
      - name: F1
        type: f1
        value: 0.0
      - name: Accuracy
        type: accuracy
        value: 0.8960890010322284
  ---
  
  <!-- 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-wnut17-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 wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6054
  - Precision: 0.0
  - Recall: 0.0
  - F1: 0.0
  - Accuracy: 0.8961
  
  ## 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: 128
  - eval_batch_size: 128
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
  | No log        | 1.0   | 27   | 1.1060          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 2.0   | 54   | 0.9075          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 3.0   | 81   | 0.7978          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 4.0   | 108  | 0.7333          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 5.0   | 135  | 0.6929          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 6.0   | 162  | 0.6661          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 7.0   | 189  | 0.6477          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 8.0   | 216  | 0.6346          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 9.0   | 243  | 0.6251          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 10.0  | 270  | 0.6182          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 11.0  | 297  | 0.6132          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 12.0  | 324  | 0.6097          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 13.0  | 351  | 0.6073          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 14.0  | 378  | 0.6059          | 0.0       | 0.0    | 0.0 | 0.8961   |
  | No log        | 15.0  | 405  | 0.6054          | 0.0       | 0.0    | 0.0 | 0.8961   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  